
@Misc{		  bannur2023learningexploittemporalstructure,
  title		= {Learning to Exploit Temporal Structure for Biomedical
		  Vision-Language Processing},
  author	= {Shruthi Bannur and Stephanie Hyland and Qianchu Liu and
		  Fernando Pérez-García and Maximilian Ilse and Daniel C.
		  Castro and Benedikt Boecking and Harshita Sharma and Kenza
		  Bouzid and Anja Thieme and Anton Schwaighofer and Maria
		  Wetscherek and Matthew P. Lungren and Aditya Nori and
		  Javier Alvarez-Valle and Ozan Oktay},
  year		= {2023},
  eprint	= {2301.04558},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/2301.04558}
}

###Misc{	  bannur2023learningexploittemporalstructure,
  title		= {Learning to Exploit Temporal Structure for Biomedical
		  Vision-Language Processing},
  author	= {Shruthi Bannur and Stephanie Hyland and Qianchu Liu and
		  Fernando Pérez-García and Maximilian Ilse and Daniel C.
		  Castro and Benedikt Boecking and Harshita Sharma and Kenza
		  Bouzid and Anja Thieme and Anton Schwaighofer and Maria
		  Wetscherek and Matthew P. Lungren and Aditya Nori and
		  Javier Alvarez-Valle and Ozan Oktay},
  year		= {2023},
  eprint	= {2301.04558},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/2301.04558}
}

@Misc{		  bannur_learning_2023,
  title		= {Learning to {Exploit} {Temporal} {Structure} for
		  {Biomedical} {Vision}-{Language} {Processing}},
  url		= {http://arxiv.org/abs/2301.04558},
  doi		= {10.48550/arXiv.2301.04558},
  abstract	= {Self-supervised learning in vision-language processing
		  exploits semantic alignment between imaging and text
		  modalities. Prior work in biomedical VLP has mostly relied
		  on the alignment of single image and report pairs even
		  though clinical notes commonly refer to prior images. This
		  does not only introduce poor alignment between the
		  modalities but also a missed opportunity to exploit rich
		  self-supervision through existing temporal content in the
		  data. In this work, we explicitly account for prior images
		  and reports when available during both training and
		  fine-tuning. Our approach, named BioViL-T, uses a
		  CNN-Transformer hybrid multi-image encoder trained jointly
		  with a text model. It is designed to be versatile to
		  arising challenges such as pose variations and missing
		  input images across time. The resulting model excels on
		  downstream tasks both in single- and multi-image setups,
		  achieving state-of-the-art performance on (I) progression
		  classification, (II) phrase grounding, and (III) report
		  generation, whilst offering consistent improvements on
		  disease classification and sentence-similarity tasks. We
		  release a novel multi-modal temporal benchmark dataset,
		  MS-CXR-T, to quantify the quality of vision-language
		  representations in terms of temporal semantics. Our
		  experimental results show the advantages of incorporating
		  prior images and reports to make most use of the data.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Bannur, Shruthi and Hyland, Stephanie and Liu, Qianchu and
		  Pérez-García, Fernando and Ilse, Maximilian and Castro,
		  Daniel C. and Boecking, Benedikt and Sharma, Harshita and
		  Bouzid, Kenza and Thieme, Anja and Schwaighofer, Anton and
		  Wetscherek, Maria and Lungren, Matthew P. and Nori, Aditya
		  and Alvarez-Valle, Javier and Oktay, Ozan},
  month		= mar,
  year		= {2023},
  note		= {arXiv:2301.04558 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Computation and Language}
}

@InProceedings{	  bannur_learning_2023-1,
  title		= {Learning to {Exploit} {Temporal} {Structure} for
		  {Biomedical} {Vision}-{Language} {Processing}},
  isbn		= {979-8-3503-0129-8},
  url		= {https://www.computer.org/csdl/proceedings-article/cvpr/2023/012900p5016/1POUXdCWrss},
  doi		= {10.1109/CVPR52729.2023.01442},
  abstract	= {Self-supervised learning in vision-language processing
		  (VLP) exploits semantic alignment between imaging and text
		  modalities. Prior work in biomedical VLP has mostly relied
		  on the alignment of single image and report pairs even
		  though clinical notes commonly refer to prior images. This
		  does not only introduce poor alignment between the
		  modalities but also a missed opportunity to exploit rich
		  self-supervision through existing temporal content in the
		  data. In this work, we explicitly account for prior images
		  and reports when available during both training and
		  fine-tuning. Our approach, named BioViL-T, uses a
		  CNN-Transformer hybrid multi-image encoder trained jointly
		  with a text model. It is designed to be versatile to
		  arising challenges such as pose variations and missing
		  input images across time. The resulting model excels on
		  downstream tasks both in single- and multi-image setups,
		  achieving state-of-the-art (SOTA) performance on (I)
		  progression classification, (II) phrase grounding, and
		  (III) report generation, whilst offering consistent
		  improvements on disease classification and
		  sentence-similarity tasks. We release a novel multi-modal
		  temporal benchmark dataset, MS-CXR-T, to quantify the
		  quality of vision-language representations in terms of
		  temporal semantics. Our experimental results show the
		  advantages of incorporating prior images and reports to
		  make most use of the data.},
  language	= {English},
  urldate	= {2025-10-10},
  publisher	= {IEEE Computer Society},
  author	= {Bannur, Shruthi and Hyland, Stephanie and Liu, Qianchu and
		  Pérez-García, Fernando and Ilse, Maximilian and Castro,
		  Daniel C. and Boecking, Benedikt and Sharma, Harshita and
		  Bouzid, Kenza and Thieme, Anja and Schwaighofer, Anton and
		  Wetscherek, Maria and Lungren, Matthew P. and Nori, Aditya
		  and Alvarez-Valle, Javier and Oktay, Ozan},
  month		= jun,
  year		= {2023},
  pages		= {15016--15027}
}

@Misc{		  bardes_revisiting_2024,
  title		= {Revisiting {Feature} {Prediction} for {Learning} {Visual}
		  {Representations} from {Video}},
  url		= {http://arxiv.org/abs/2404.08471},
  doi		= {10.48550/arXiv.2404.08471},
  abstract	= {This paper explores feature prediction as a stand-alone
		  objective for unsupervised learning from video and
		  introduces V-JEPA, a collection of vision models trained
		  solely using a feature prediction objective, without the
		  use of pretrained image encoders, text, negative examples,
		  reconstruction, or other sources of supervision. The models
		  are trained on 2 million videos collected from public
		  datasets and are evaluated on downstream image and video
		  tasks. Our results show that learning by predicting video
		  features leads to versatile visual representations that
		  perform well on both motion and appearance-based tasks,
		  without adaption of the model's parameters; e.g., using a
		  frozen backbone. Our largest model, a ViT-H/16 trained only
		  on videos, obtains 81.9\% on Kinetics-400, 72.2\% on
		  Something-Something-v2, and 77.9\% on ImageNet1K.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Bardes, Adrien and Garrido, Quentin and Ponce, Jean and
		  Chen, Xinlei and Rabbat, Michael and LeCun, Yann and
		  Assran, Mahmoud and Ballas, Nicolas},
  month		= feb,
  year		= {2024},
  note		= {arXiv:2404.08471 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning, Computer
		  Science - Artificial Intelligence}
}

@Misc{		  bica2020estimatingcounterfactualtreatmentoutcomes,
  title		= {Estimating Counterfactual Treatment Outcomes over Time
		  Through Adversarially Balanced Representations},
  author	= {Ioana Bica and Ahmed M. Alaa and James Jordon and Mihaela
		  van der Schaar},
  year		= {2020},
  eprint	= {2002.04083},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2002.04083}
}

@Article{	  bird_artificial_2022,
  title		= {Artificial intelligence and the future of radiographic
		  scoring in rheumatoid arthritis: a viewpoint},
  volume	= {24},
  issn		= {1478-6362},
  shorttitle	= {Artificial intelligence and the future of radiographic
		  scoring in rheumatoid arthritis},
  url		= {https://doi.org/10.1186/s13075-022-02972-x},
  doi		= {10.1186/s13075-022-02972-x},
  abstract	= {Rheumatoid arthritis is an autoimmune condition that
		  predominantly affects the synovial joints, causing joint
		  destruction, pain, and disability. Historically, the
		  standard for measuring the long-term efficacy of
		  disease-modifying antirheumatic drugs has been the
		  assessment of plain radiographs with scoring techniques
		  that quantify joint damage. However, with significant
		  improvements in therapy, current radiographic scoring
		  systems may no longer be fit for purpose for the milder
		  spectrum of disease seen today. We argue that artificial
		  intelligence is an apt solution to further improve upon
		  radiographic scoring, as it can readily learn to recognize
		  subtle patterns in imaging data to not only improve
		  efficiency, but can also increase the sensitivity to
		  variation in mild disease. Current work in the area
		  demonstrates the feasibility of automating scoring but is
		  yet to take full advantage of the strengths of artificial
		  intelligence. By fully leveraging the power of artificial
		  intelligence, faster and more sensitive scoring could
		  enable the ongoing development of effective treatments for
		  patients with rheumatoid arthritis.},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {Arthritis Research \& Therapy},
  author	= {Bird, Alix and Oakden-Rayner, Lauren and McMaster,
		  Christopher and Smith, Luke A. and Zeng, Minyan and
		  Wechalekar, Mihir D. and Ray, Shonket and Proudman, Susanna
		  and Palmer, Lyle J.},
  month		= dec,
  year		= {2022},
  keywords	= {Deep learning, Artificial intelligence, Radiographic
		  scoring, Rheumatoid arthritis},
  pages		= {268}
}

@Misc{		  bo2024deeplearningmodelsautomate,
  title		= {Deep Learning Models to Automate the Scoring of Hand
		  Radiographs for Rheumatoid Arthritis},
  author	= {Zhiyan Bo and Laura C. Coates and Bartlomiej W. Papiez},
  year		= {2024},
  eprint	= {2406.09980},
  archiveprefix	= {arXiv},
  primaryclass	= {eess.IV},
  url		= {https://arxiv.org/abs/2406.09980}
}

@Misc{		  bo_deep_2024,
  title		= {Deep {Learning} {Models} to {Automate} the {Scoring} of
		  {Hand} {Radiographs} for {Rheumatoid} {Arthritis}},
  url		= {http://arxiv.org/abs/2406.09980},
  doi		= {10.48550/arXiv.2406.09980},
  abstract	= {The van der Heijde modification of the Sharp (SvdH) score
		  is a widely used radiographic scoring method to quantify
		  damage in Rheumatoid Arthritis (RA) in clinical trials.
		  However, its complexity with a necessity to score each
		  individual joint, and the expertise required limit its
		  application in clinical practice, especially in disease
		  progression measurement. In this work, we addressed this
		  limitation by developing a bespoke, automated pipeline that
		  is capable of predicting the SvdH score and RA severity
		  from hand radiographs without the need to localise the
		  joints first. Using hand radiographs from RA and suspected
		  RA patients, we first investigated the performance of the
		  state-of-the-art architectures in predicting the total SvdH
		  score for hands and wrists and its corresponding severity
		  class. Secondly, we leveraged publicly available data sets
		  to perform transfer learning with different finetuning
		  schemes and ensemble learning, which resulted in
		  substantial improvement in model performance being on par
		  with an experienced human reader. The best model for RA
		  scoring achieved a Pearson's correlation coefficient (PCC)
		  of 0.925 and root mean squared error (RMSE) of 18.02, while
		  the best model for RA severity classification achieved an
		  accuracy of 0.358 and PCC of 0.859. Our score prediction
		  model attained almost comparable accuracy with experienced
		  radiologists (PCC = 0.97, RMSE = 18.75). Finally, using
		  Grad-CAM, we showed that our models could focus on the
		  anatomical structures in hands and wrists which clinicians
		  deemed as relevant to RA progression in the majority of
		  cases.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Bo, Zhiyan and Coates, Laura C. and Papiez, Bartlomiej
		  W.},
  month		= jun,
  year		= {2024},
  note		= {arXiv:2406.09980 [eess]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Electrical Engineering and Systems Science -
		  Image and Video Processing}
}

@Misc{		  bo_interpretable_2025,
  title		= {Interpretable {Rheumatoid} {Arthritis} {Scoring} via
		  {Anatomy}-aware {Multiple} {Instance} {Learning}},
  url		= {http://arxiv.org/abs/2508.06218},
  doi		= {10.48550/arXiv.2508.06218},
  abstract	= {The Sharp/van der Heijde (SvdH) score has been widely used
		  in clinical trials to quantify radiographic damage in
		  Rheumatoid Arthritis (RA), but its complexity has limited
		  its adoption in routine clinical practice. To address the
		  inefficiency of manual scoring, this work proposes a
		  two-stage pipeline for interpretable image-level SvdH score
		  prediction using dual-hand radiographs. Our approach
		  extracts disease-relevant image regions and integrates them
		  using attention-based multiple instance learning to
		  generate image-level features for prediction. We propose
		  two region extraction schemes: 1) sampling image tiles most
		  likely to contain abnormalities, and 2) cropping patches
		  containing disease-relevant joints. With Scheme 2, our best
		  individual score prediction model achieved a Pearson's
		  correlation coefficient (PCC) of 0.943 and a root mean
		  squared error (RMSE) of 15.73. Ensemble learning further
		  boosted prediction accuracy, yielding a PCC of 0.945 and
		  RMSE of 15.57, achieving state-of-the-art performance that
		  is comparable to that of experienced radiologists (PCC =
		  0.97, RMSE = 18.75). Finally, our pipeline effectively
		  identified and made decisions based on anatomical
		  structures which clinicians consider relevant to RA
		  progression.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Bo, Zhiyan and Coates, Laura C. and Papiez, Bartlomiej
		  W.},
  month		= aug,
  year		= {2025},
  note		= {arXiv:2508.06218 [cs] version: 1},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition}
}

@Misc{		  bo_interpretable_2025-1,
  title		= {Interpretable {Rheumatoid} {Arthritis} {Scoring} via
		  {Anatomy}-aware {Multiple} {Instance} {Learning}},
  url		= {http://arxiv.org/abs/2508.06218},
  doi		= {10.48550/arXiv.2508.06218},
  abstract	= {The Sharp/van der Heijde (SvdH) score has been widely used
		  in clinical trials to quantify radiographic damage in
		  Rheumatoid Arthritis (RA), but its complexity has limited
		  its adoption in routine clinical practice. To address the
		  inefficiency of manual scoring, this work proposes a
		  two-stage pipeline for interpretable image-level SvdH score
		  prediction using dual-hand radiographs. Our approach
		  extracts disease-relevant image regions and integrates them
		  using attention-based multiple instance learning to
		  generate image-level features for prediction. We propose
		  two region extraction schemes: 1) sampling image tiles most
		  likely to contain abnormalities, and 2) cropping patches
		  containing disease-relevant joints. With Scheme 2, our best
		  individual score prediction model achieved a Pearson's
		  correlation coefficient (PCC) of 0.943 and a root mean
		  squared error (RMSE) of 15.73. Ensemble learning further
		  boosted prediction accuracy, yielding a PCC of 0.945 and
		  RMSE of 15.57, achieving state-of-the-art performance that
		  is comparable to that of experienced radiologists (PCC =
		  0.97, RMSE = 18.75). Finally, our pipeline effectively
		  identified and made decisions based on anatomical
		  structures which clinicians consider relevant to RA
		  progression.},
  urldate	= {2025-10-09},
  publisher	= {arXiv},
  author	= {Bo, Zhiyan and Coates, Laura C. and Papiez, Bartlomiej
		  W.},
  month		= aug,
  year		= {2025},
  note		= {arXiv:2508.06218 [cs] version: 1},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition}
}

@InProceedings{	  boecking_making_2022,
  address	= {Cham},
  title		= {Making the {Most} of {Text} {Semantics} to {Improve}
		  {Biomedical} {Vision}–{Language} {Processing}},
  isbn		= {978-3-031-20059-5},
  doi		= {10.1007/978-3-031-20059-5_1},
  abstract	= {Multi-modal data abounds in biomedicine, such as radiology
		  images and reports. Interpreting this data at scale is
		  essential for improving clinical care and accelerating
		  clinical research. Biomedical text with its complex
		  semantics poses additional challenges in vision–language
		  modelling compared to the general domain, and previous work
		  has used insufficiently adapted models that lack
		  domain-specific language understanding. In this paper, we
		  show that principled textual semantic modelling can
		  substantially improve contrastive learning in
		  self-supervised vision–language processing. We release a
		  language model that achieves state-of-the-art results in
		  radiology natural language inference through its improved
		  vocabulary and novel language pretraining objective
		  leveraging semantics and discourse characteristics in
		  radiology reports. Further, we propose a self-supervised
		  joint vision–language approach with a focus on better
		  text modelling. It establishes new state of the art results
		  on a wide range of publicly available benchmarks, in part
		  by leveraging our new domain-specific language model. We
		  release a new dataset with locally-aligned phrase grounding
		  annotations by radiologists to facilitate the study of
		  complex semantic modelling in biomedical vision–language
		  processing. A broad evaluation, including on this new
		  dataset, shows that our contrastive learning approach,
		  aided by textual-semantic modelling, outperforms prior
		  methods in segmentation tasks, despite only using a
		  global-alignment objective.},
  language	= {en},
  booktitle	= {Computer {Vision} – {ECCV} 2022},
  publisher	= {Springer Nature Switzerland},
  author	= {Boecking, Benedikt and Usuyama, Naoto and Bannur, Shruthi
		  and Castro, Daniel C. and Schwaighofer, Anton and Hyland,
		  Stephanie and Wetscherek, Maria and Naumann, Tristan and
		  Nori, Aditya and Alvarez-Valle, Javier and Poon, Hoifung
		  and Oktay, Ozan},
  editor	= {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha
		  and Farinella, Giovanni Maria and Hassner, Tal},
  year		= {2022},
  keywords	= {Multi-modal, Radiology, Self-supervision, Weak
		  supervision},
  pages		= {1--21}
}

@InProceedings{	  boecking_making_2022-1,
  address	= {Cham},
  title		= {Making the {Most} of {Text} {Semantics} to {Improve}
		  {Biomedical} {Vision}–{Language} {Processing}},
  isbn		= {978-3-031-20059-5},
  doi		= {10.1007/978-3-031-20059-5_1},
  abstract	= {Multi-modal data abounds in biomedicine, such as radiology
		  images and reports. Interpreting this data at scale is
		  essential for improving clinical care and accelerating
		  clinical research. Biomedical text with its complex
		  semantics poses additional challenges in vision–language
		  modelling compared to the general domain, and previous work
		  has used insufficiently adapted models that lack
		  domain-specific language understanding. In this paper, we
		  show that principled textual semantic modelling can
		  substantially improve contrastive learning in
		  self-supervised vision–language processing. We release a
		  language model that achieves state-of-the-art results in
		  radiology natural language inference through its improved
		  vocabulary and novel language pretraining objective
		  leveraging semantics and discourse characteristics in
		  radiology reports. Further, we propose a self-supervised
		  joint vision–language approach with a focus on better
		  text modelling. It establishes new state of the art results
		  on a wide range of publicly available benchmarks, in part
		  by leveraging our new domain-specific language model. We
		  release a new dataset with locally-aligned phrase grounding
		  annotations by radiologists to facilitate the study of
		  complex semantic modelling in biomedical vision–language
		  processing. A broad evaluation, including on this new
		  dataset, shows that our contrastive learning approach,
		  aided by textual-semantic modelling, outperforms prior
		  methods in segmentation tasks, despite only using a
		  global-alignment objective.},
  language	= {en},
  booktitle	= {Computer {Vision} – {ECCV} 2022},
  publisher	= {Springer Nature Switzerland},
  author	= {Boecking, Benedikt and Usuyama, Naoto and Bannur, Shruthi
		  and Castro, Daniel C. and Schwaighofer, Anton and Hyland,
		  Stephanie and Wetscherek, Maria and Naumann, Tristan and
		  Nori, Aditya and Alvarez-Valle, Javier and Poon, Hoifung
		  and Oktay, Ozan},
  editor	= {Avidan, Shai and Brostow, Gabriel and Cissé, Moustapha
		  and Farinella, Giovanni Maria and Hassner, Tal},
  year		= {2022},
  keywords	= {Multi-modal, Radiology, Self-supervision, Weak
		  supervision},
  pages		= {1--21}
}

@Misc{		  bouchattaoui2024causalcontrastivelearningcounterfactual,
  title		= {Causal Contrastive Learning for Counterfactual Regression
		  Over Time},
  author	= {Mouad El Bouchattaoui and Myriam Tami and Benoit Lepetit
		  and Paul-Henry Cournède},
  year		= {2024},
  eprint	= {2406.00535},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2406.00535}
}

@InProceedings{	  chakravarty:miccai2024:forecasting_disease_progression_with_parallel_hyperplanes,
  author	= { Chakravarty, Arunava and Emre, Taha and Lachinov, Dmitrii
		  and Rivail, Antoine and Scholl, Hendrik P. N. and Fritsche,
		  Lars and Sivaprasad, Sobha and Rueckert, Daniel and Lotery,
		  Andrew and Schmidt-Erfurth, Ursula and Bogunović, Hrvoje},
  title		= { { Forecasting Disease Progression with Parallel
		  Hyperplanes in Longitudinal Retinal OCT } },
  booktitle	= {proceedings of Medical Image Computing and Computer
		  Assisted Intervention -- MICCAI 2024},
  year		= {2024},
  publisher	= {Springer Nature Switzerland},
  volume	= {LNCS 15005},
  month		= {October},
  page		= {273 -- 283}
}

@Misc{		  chen2019neuralordinarydifferentialequations,
  title		= {Neural Ordinary Differential Equations},
  author	= {Ricky T. Q. Chen and Yulia Rubanova and Jesse Bettencourt
		  and David Duvenaud},
  year		= {2019},
  eprint	= {1806.07366},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/1806.07366}
}

@Misc{		  chen2020simpleframeworkcontrastivelearning,
  title		= {A Simple Framework for Contrastive Learning of Visual
		  Representations},
  author	= {Ting Chen and Simon Kornblith and Mohammad Norouzi and
		  Geoffrey Hinton},
  year		= {2020},
  eprint	= {2002.05709},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2002.05709}
}

@Misc{		  codella_medimageinsight_2024,
  title		= {{MedImageInsight}: {An} {Open}-{Source} {Embedding}
		  {Model} for {General} {Domain} {Medical} {Imaging}},
  shorttitle	= {{MedImageInsight}},
  url		= {http://arxiv.org/abs/2410.06542},
  doi		= {10.48550/arXiv.2410.06542},
  abstract	= {In this work, we present MedImageInsight, an open-source
		  medical imaging embedding model. MedImageInsight is trained
		  on medical images with associated text and labels across a
		  diverse collection of domains, including X-Ray, CT, MRI,
		  dermoscopy, OCT, fundus photography, ultrasound,
		  histopathology, and mammography. Rigorous evaluations
		  demonstrate MedImageInsight's ability to achieve
		  state-of-the-art (SOTA) or human expert level performance
		  across classification, image-image search, and fine-tuning
		  tasks. Specifically, on public datasets, MedImageInsight
		  achieves SOTA in CT 3D medical image retrieval, as well as
		  SOTA in disease classification and search for chest X-ray,
		  dermatology, and OCT imaging. Furthermore, MedImageInsight
		  achieves human expert performance in bone age estimation
		  (on both public and partner data), as well as AUC above 0.9
		  in most other domains. When paired with a text decoder,
		  MedImageInsight achieves near SOTA level single image
		  report findings generation with less than
		  10{\textbackslash}\% the parameters of other models.
		  Compared to fine-tuning GPT-4o with only MIMIC-CXR data for
		  the same task, MedImageInsight outperforms in clinical
		  metrics, but underperforms on lexical metrics where GPT-4o
		  sets a new SOTA. Importantly for regulatory purposes,
		  MedImageInsight can generate ROC curves, adjust sensitivity
		  and specificity based on clinical need, and provide
		  evidence-based decision support through image-image search
		  (which can also enable retrieval augmented generation). In
		  an independent clinical evaluation of image-image search in
		  chest X-ray, MedImageInsight outperformed every other
		  publicly available foundation model evaluated by large
		  margins (over 6 points AUC), and significantly outperformed
		  other models in terms of AI fairness (across age and
		  gender). We hope releasing MedImageInsight will help
		  enhance collective progress in medical imaging AI research
		  and development.},
  urldate	= {2025-11-10},
  publisher	= {arXiv},
  author	= {Codella, Noel C. F. and Jin, Ying and Jain, Shrey and Gu,
		  Yu and Lee, Ho Hin and Abacha, Asma Ben and
		  Santamaria-Pang, Alberto and Guyman, Will and Sangani,
		  Naiteek and Zhang, Sheng and Poon, Hoifung and Hyland,
		  Stephanie and Bannur, Shruthi and Alvarez-Valle, Javier and
		  Li, Xue and Garrett, John and McMillan, Alan and Rajguru,
		  Gaurav and Maddi, Madhu and Vijayrania, Nilesh and Bhimai,
		  Rehaan and Mecklenburg, Nick and Jain, Rupal and Holstein,
		  Daniel and Gaur, Naveen and Aski, Vijay and Hwang,
		  Jenq-Neng and Lin, Thomas and Tarapov, Ivan and Lungren,
		  Matthew and Wei, Mu},
  month		= oct,
  year		= {2024},
  note		= {arXiv:2410.06542 [eess]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Electrical Engineering and Systems Science -
		  Image and Video Processing}
}

@InProceedings{	  couronne2021longitudinal,
  title		= {Longitudinal self-supervision to disentangle inter-patient
		  variability from disease progression},
  author	= {Couronn{\'e}, Rapha{\"e}l and Vernhet, Paul and Durrleman,
		  Stanley},
  booktitle	= {International Conference on Medical Image Computing and
		  Computer-Assisted Intervention},
  pages		= {231--241},
  year		= {2021},
  organization	= {Springer}
}

@Article{	  de_almeida_foundation_2025,
  title		= {Foundation models for radiology—the position of the {AI}
		  for {Health} {Imaging} ({AI4HI}) network},
  volume	= {16},
  issn		= {1869-4101},
  url		= {https://pmc.ncbi.nlm.nih.gov/articles/PMC12328884/},
  doi		= {10.1186/s13244-025-02056-9},
  abstract	= {Abstract Foundation models are large models trained on big
		  data which can be used for downstream tasks. In radiology,
		  these models can potentially address several gaps in
		  fairness and generalization, as they can be trained on
		  massive datasets without labelled data and adapted to tasks
		  requiring data with a small number of descriptions. This
		  reduces one of the limiting bottlenecks in clinical model
		  construction—data annotation—as these models can be
		  trained through a variety of techniques that require little
		  more than radiological images with or without their
		  corresponding radiological reports. However, foundation
		  models may be insufficient as they are affected—to a
		  smaller extent when compared with traditional supervised
		  learning approaches—by the same issues that lead to
		  underperforming models, such as a lack of
		  transparency/explainability, and biases. To address these
		  issues, we advocate that the development of foundation
		  models should not only be pursued but also accompanied by
		  the development of a decentralized clinical validation and
		  continuous training framework. This does not guarantee the
		  resolution of the problems associated with foundation
		  models, but it enables developers, clinicians and patients
		  to know when, how and why models should be updated,
		  creating a clinical AI ecosystem that is better capable of
		  serving all stakeholders.
		  
		  Critical relevance statement Foundation models may mitigate
		  issues like bias and poor generalization in radiology AI,
		  but challenges persist. We propose a decentralized,
		  cross-institutional framework for continuous validation and
		  training to enhance model reliability, safety, and clinical
		  utility.
		  
		  Key Points
		  
		  Foundation models trained on large datasets reduce
		  annotation burdens and improve fairness and generalization
		  in radiology.Despite improvements, they still face
		  challenges like limited transparency, explainability, and
		  residual biases.A decentralized, cross-institutional
		  framework for clinical validation and continuous training
		  can strengthen reliability and inclusivity in clinical AI.
		  
		  Graphical Abstract},
  urldate	= {2025-10-07},
  journal	= {Insights into Imaging},
  author	= {de Almeida, José Guilherme and Alberich, Leonor Cerdá
		  and Tsakou, Gianna and Marias, Kostas and Tsiknakis,
		  Manolis and Lekadir, Karim and Marti-Bonmati, Luis and
		  Papanikolaou, Nikolaos},
  month		= aug,
  year		= {2025},
  pmid		= {40770121},
  pmcid		= {PMC12328884},
  pages		= {168}
}

@Article{	  deimel2025autoscora,
  author	= {Deimel, Thomas and Weiser, Paul J. and Urschler, Martin
		  and Payer, Christian and Mandl, Peter and Langs, Georg and
		  Aletaha, Daniel},
  title		= {autoscoRA: Deep Learning to Automate Sharp/van der Heijde
		  Scoring of Radiographic Damage in Rheumatoid Arthritis},
  elocation-id	= {2025.12.26.25343056},
  year		= {2025},
  doi		= {10.64898/2025.12.26.25343056},
  publisher	= {Cold Spring Harbor Laboratory Press},
  abstract	= {Objective Regular imaging by conventional radiography to
		  assess for joint damage is a cornerstone in the management
		  of rheumatoid arthritis (RA). Scoring systems to quantify
		  such damage, such as the widely used Sharp/van der Heijde
		  (SvdH) score, are limited by the requirement of time and
		  experienced staff as well as intra- and inter-rater
		  variability. To alleviate these problems, autoscoRA, a
		  fully automated scoring system to assign SvdH scores to
		  radiographs of the hands and feet was developed.Methods
		  Using the hitherto largest dataset of adult rheumatoid
		  arthritis patients, autoscoRA, a deep learning-based
		  system, was trained to automatically perform joint
		  extraction and scoring of joint space narrowing and bone
		  erosion.Results The dataset included 769 patients (155 of
		  which in the test set) with 3437 visits (707) and 12144
		  radiographs (2507). The model reached excellent agreement
		  with a human scorer for joint space narrowing, erosion, and
		  combined scores both on the joint level and for summed
		  total SvdH scores (ICC 0.9). On a subset of data scored by
		  a second human reader, the model outperformed the former in
		  terms of agreement with the first human reader. In
		  addition, autoscoRA demonstrated good agreement with a
		  human reader for detecting longitudinal progression of
		  joint damage across different SvdH score cut-offs defining
		  the presence of progression (average agreement of 70
		  \%).Conclusion Automated systems like autoscoRA could be
		  used to facilitate scoring of radiographic joint damage in
		  clinical trials, registries and observational studies, and,
		  eventually, routine clinical care.Competing Interest
		  StatementThe authors have declared no competing
		  interest.Funding StatementThis project is supported by the
		  Innovative Health Initiative Joint Undertaking (IHI JU)
		  under grant agreement No 101194766. The JU receives support
		  from the European Unions Horizon Europe research and
		  innovation programme, COCIR, EFPIA, Europa Bio, MedTech
		  Europe, Vaccines Europe, Collective Minds and Radiology AB,
		  MoonLake Immunotherapeutics AG, and Scienta Lab. The Swiss
		  consortium partners, EULAR and CHUV, receive funding from
		  the Swiss State Secretariat for Education, Research and
		  Innovation (SERI).Author DeclarationsI confirm all relevant
		  ethical guidelines have been followed, and any necessary
		  IRB and/or ethics committee approvals have been
		  obtained.YesThe details of the IRB/oversight body that
		  provided approval or exemption for the research described
		  are given below:The project was approved by the Ethics
		  Committee of the Medical University of Vienna (EC No.
		  1206/2018).I confirm that all necessary patient/participant
		  consent has been obtained and the appropriate institutional
		  forms have been archived, and that any
		  patient/participant/sample identifiers included were not
		  known to anyone (e.g., hospital staff, patients or
		  participants themselves) outside the research group so
		  cannot be used to identify individuals.YesI understand that
		  all clinical trials and any other prospective
		  interventional studies must be registered with an
		  ICMJE-approved registry, such as ClinicalTrials.gov. I
		  confirm that any such study reported in the manuscript has
		  been registered and the trial registration ID is provided
		  (note: if posting a prospective study registered
		  retrospectively, please provide a statement in the trial ID
		  field explaining why the study was not registered in
		  advance).YesI have followed all appropriate research
		  reporting guidelines, such as any relevant EQUATOR Network
		  research reporting checklist(s) and other pertinent
		  material, if applicable.Yes},
  url		= {https://www.medrxiv.org/content/early/2025/12/29/2025.12.26.25343056},
  eprint	= {https://www.medrxiv.org/content/early/2025/12/29/2025.12.26.25343056.full.pdf},
  journal	= {medRxiv}
}

@Misc{		  gong2022ranksimrankingsimilarityregularization,
  title		= {RankSim: Ranking Similarity Regularization for Deep
		  Imbalanced Regression},
  author	= {Yu Gong and Greg Mori and Frederick Tung},
  year		= {2022},
  eprint	= {2205.15236},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2205.15236}
}

@Book{		  goodfellow-et-al-2016,
  title		= {Deep Learning},
  author	= {Ian Goodfellow and Yoshua Bengio and Aaron Courville},
  publisher	= {MIT Press},
  note		= {\texttt{http://www.deeplearningbook.org}},
  year		= {2016}
}

@InProceedings{	  govind_vision_2025,
  address	= {Cham},
  title		= {Vision {Transformer} {Model} for {Automated}
		  {End}-to-{End} {Radiographic} {Assessment} of {Joint}
		  {Damage} in {Psoriatic} {Arthritis}},
  isbn		= {978-3-031-73284-3},
  abstract	= {Deep learning techniques such as Convolutional Neural
		  Networks (CNNs) and Vision Transformers (ViTs) have
		  demonstrated strong capabilities in region-of-interest
		  (ROI) detection and disease severity scoring, aiming to
		  reduce the manual workload for clinicians. However, studies
		  encompassing an end-to-end method for ROI detection, their
		  subsequent scoring, and aggregation to a patient level
		  score in psoriatic arthritis (PsA) have been limited. To
		  address this gap, we have developed an end-to-end multistep
		  approach for automated radiographic assessment in PsA that
		  detects joints-of-interest from X-rays, assigns structural
		  damage scores to the identified joints, and aggregates them
		  to an extremity level, and subsequently to a patient level
		  score. Furthermore, we compare our approach with
		  state-of-the-art methods and human experts. Our results
		  demonstrate the strong performance of our joint detection
		  models, achieving an average intersection over union (IoU)
		  value of 0.88 for foot joints and 0.76 for hand joints. The
		  subsequent scoring models for structural damage show
		  excellent (intra-class correlation coefficient
		  (ICC)þinspace{\textgreater}þinspace0.90) agreement with
		  an ICC of 0.94, 0.98, and 0.97 for joint level erosion,
		  JSN, and patient level scores, respectively, when compared
		  to human readers.},
  booktitle	= {Machine {Learning} in {Medical} {Imaging}},
  publisher	= {Springer Nature Switzerland},
  author	= {Govind, Darshana and Gao, Zijun and Parmar, Chaitanya and
		  Broos, Kenneth and Fountoulakis, Nicholas and Noonan,
		  Lenore and Yamamoto, Shinobu and Zemlianskaia, Natalia and
		  Meyer, Craig S. and Scherer, Emily and Deman, Michael and
		  Damasceno, Pablo and Murphy, Philip S. and Rooney, Terence
		  and Hsia, Elizabeth and Beutler, Anna and Janiczek, Robert
		  and Yip, Stephen S. F. and Standish, Kristopher},
  editor	= {Xu, Xuanang and Cui, Zhiming and Rekik, Islem and Ouyang,
		  Xi and Sun, Kaicong},
  year		= {2025},
  pages		= {94--103}
}

@Misc{		  hager_best_2023,
  title		= {Best of {Both} {Worlds}: {Multimodal} {Contrastive}
		  {Learning} with {Tabular} and {Imaging} {Data}},
  shorttitle	= {Best of {Both} {Worlds}},
  url		= {http://arxiv.org/abs/2303.14080},
  doi		= {10.48550/arXiv.2303.14080},
  abstract	= {Medical datasets and especially biobanks, often contain
		  extensive tabular data with rich clinical information in
		  addition to images. In practice, clinicians typically have
		  less data, both in terms of diversity and scale, but still
		  wish to deploy deep learning solutions. Combined with
		  increasing medical dataset sizes and expensive annotation
		  costs, the necessity for unsupervised methods that can
		  pretrain multimodally and predict unimodally has risen. To
		  address these needs, we propose the first self-supervised
		  contrastive learning framework that takes advantage of
		  images and tabular data to train unimodal encoders. Our
		  solution combines SimCLR and SCARF, two leading contrastive
		  learning strategies, and is simple and effective. In our
		  experiments, we demonstrate the strength of our framework
		  by predicting risks of myocardial infarction and coronary
		  artery disease (CAD) using cardiac MR images and 120
		  clinical features from 40,000 UK Biobank subjects.
		  Furthermore, we show the generalizability of our approach
		  to natural images using the DVM car advertisement dataset.
		  We take advantage of the high interpretability of tabular
		  data and through attribution and ablation experiments find
		  that morphometric tabular features, describing size and
		  shape, have outsized importance during the contrastive
		  learning process and improve the quality of the learned
		  embeddings. Finally, we introduce a novel form of
		  supervised contrastive learning, label as a feature (LaaF),
		  by appending the ground truth label as a tabular feature
		  during multimodal pretraining, outperforming all supervised
		  contrastive baselines.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Hager, Paul and Menten, Martin J. and Rueckert, Daniel},
  month		= mar,
  year		= {2023},
  note		= {arXiv:2303.14080 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition}
}

@Misc{		  he2015deepresiduallearningimage,
  title		= {Deep Residual Learning for Image Recognition},
  author	= {Kaiming He and Xiangyu Zhang and Shaoqing Ren and Jian
		  Sun},
  year		= {2015},
  eprint	= {1512.03385},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/1512.03385}
}

@Misc{		  hinton:arxiv:2015:distilling,
  title		= {Distilling the knowledge in a neural network},
  author	= {Hinton, Geoffrey and Vinyals, Oriol and Dean, Jeff},
  opteprinttype	= {arXiv},
  opteprint	= {1503.02531},
  howpublished	= {Eprint
		  \href{http://arxiv.org/abs/1503.02531}{arXiv:1503.02531}},
  opturl	= {http://arxiv.org/abs/1503.02531},
  year		= 2015
}

@Article{	  holland:mia2024:metadata_enhanced_learning_oct,
  title		= {Metadata-enhanced contrastive learning from retinal
		  optical coherence tomography images},
  journal	= {Medical Image Analysis},
  volume	= {97},
  pages		= {103296},
  year		= {2024},
  issn		= {1361-8415},
  doi		= {https://doi.org/10.1016/j.media.2024.103296},
  url		= {https://www.sciencedirect.com/science/article/pii/S1361841524002214},
  author	= {Robbie Holland and Oliver Leingang and Hrvoje Bogunović
		  and Sophie Riedl and Lars Fritsche and Toby Prevost and
		  Hendrik P.N. Scholl and Ursula Schmidt-Erfurth and Sobha
		  Sivaprasad and Andrew J. Lotery and Daniel Rueckert and
		  Martin J. Menten},
  keywords	= {Self-supervised learning, Contrastive learning, Retinal
		  OCT, Medical metadata, Longitudinal data}
}

@Article{	  honda10.1093_rheumatology_keac586,
  author	= {Honda, Suguru and Yano, Koichiro and Tanaka, Eiichi and
		  Ikari, Katsunori and Harigai, Masayoshi},
  title		= {Development of a scoring model for the Sharp/van der
		  Heijde score using convolutional neural networks and its
		  clinical application},
  journal	= {Rheumatology},
  volume	= {62},
  number	= {6},
  pages		= {2272-2283},
  year		= {2022},
  month		= {10},
  issn		= {1462-0324},
  doi		= {10.1093/rheumatology/keac586},
  url		= {https://doi.org/10.1093/rheumatology/keac586},
  eprint	= {https://academic.oup.com/rheumatology/article-pdf/62/6/2272/50500695/keac586.pdf}
}

@Article{	  huang_fusion_2020,
  title		= {Fusion of medical imaging and electronic health records
		  using deep learning: a systematic review and implementation
		  guidelines},
  volume	= {3},
  copyright	= {2020 The Author(s)},
  issn		= {2398-6352},
  shorttitle	= {Fusion of medical imaging and electronic health records
		  using deep learning},
  url		= {https://www.nature.com/articles/s41746-020-00341-z},
  doi		= {10.1038/s41746-020-00341-z},
  abstract	= {Advancements in deep learning techniques carry the
		  potential to make significant contributions to healthcare,
		  particularly in fields that utilize medical imaging for
		  diagnosis, prognosis, and treatment decisions. The current
		  state-of-the-art deep learning models for radiology
		  applications consider only pixel-value information without
		  data informing clinical context. Yet in practice, pertinent
		  and accurate non-imaging data based on the clinical history
		  and laboratory data enable physicians to interpret imaging
		  findings in the appropriate clinical context, leading to a
		  higher diagnostic accuracy, informative clinical decision
		  making, and improved patient outcomes. To achieve a similar
		  goal using deep learning, medical imaging pixel-based
		  models must also achieve the capability to process
		  contextual data from electronic health records (EHR) in
		  addition to pixel data. In this paper, we describe
		  different data fusion techniques that can be applied to
		  combine medical imaging with EHR, and systematically review
		  medical data fusion literature published between 2012 and
		  2020. We conducted a systematic search on PubMed and Scopus
		  for original research articles leveraging deep learning for
		  fusion of multimodality data. In total, we screened 985
		  studies and extracted data from 17 papers. By means of this
		  systematic review, we present current knowledge, summarize
		  important results and provide implementation guidelines to
		  serve as a reference for researchers interested in the
		  application of multimodal fusion in medical imaging.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {npj Digital Medicine},
  author	= {Huang, Shih-Cheng and Pareek, Anuj and Seyyedi, Saeed and
		  Banerjee, Imon and Lungren, Matthew P.},
  month		= oct,
  year		= {2020},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Medical imaging, Machine learning, Data integration},
  pages		= {136}
}

@InProceedings{	  huang_gloria_2021,
  title		= {{GLoRIA}: {A} {Multimodal} {Global}-{Local}
		  {Representation} {Learning} {Framework} for
		  {Label}-efficient {Medical} {Image} {Recognition}},
  shorttitle	= {{GLoRIA}},
  url		= {https://ieeexplore.ieee.org/document/9710099},
  doi		= {10.1109/ICCV48922.2021.00391},
  abstract	= {In recent years, the growing utilization of medical
		  imaging is placing an increasing burden on radiologists.
		  Deep learning provides a promising solution for automatic
		  medical image analysis and clinical decision support.
		  However, large-scale manually labeled datasets required for
		  training deep neural networks are difficult and expensive
		  to obtain for medical images. The purpose of this work is
		  to develop label-efficient multimodal medical imaging
		  representations by leveraging radiology reports. We propose
		  an attention-based framework for learning global and local
		  representations by contrasting image sub-regions and words
		  in the paired report. In addition, we propose methods to
		  leverage the learned representations for various downstream
		  medical image recognition tasks with limited labels. Our
		  results demonstrate high-performance and label-efficiency
		  for image-text retrieval, classification (finetuning and
		  zerosshot settings), and segmentation on different
		  datasets.},
  urldate	= {2025-10-07},
  booktitle	= {2021 {IEEE}/{CVF} {International} {Conference} on
		  {Computer} {Vision} ({ICCV})},
  author	= {Huang, Shih-Cheng and Shen, Liyue and Lungren, Matthew P.
		  and Yeung, Serena},
  month		= oct,
  year		= {2021},
  note		= {ISSN: 2380-7504},
  keywords	= {and cell microscopy, biological, Deep learning, Image
		  analysis, Image recognition, Image segmentation, Medical,
		  Neural networks, Representation learning, Training,
		  Transfer/Low-shot/Semi/Unsupervised Learning, Vision +
		  language},
  pages		= {3922--3931}
}

@Article{	  huang_self-supervised_2023,
  title		= {Self-supervised learning for medical image classification:
		  a systematic review and implementation guidelines},
  volume	= {6},
  copyright	= {2023 The Author(s)},
  issn		= {2398-6352},
  shorttitle	= {Self-supervised learning for medical image
		  classification},
  url		= {https://www.nature.com/articles/s41746-023-00811-0},
  doi		= {10.1038/s41746-023-00811-0},
  abstract	= {Advancements in deep learning and computer vision provide
		  promising solutions for medical image analysis, potentially
		  improving healthcare and patient outcomes. However, the
		  prevailing paradigm of training deep learning models
		  requires large quantities of labeled training data, which
		  is both time-consuming and cost-prohibitive to curate for
		  medical images. Self-supervised learning has the potential
		  to make significant contributions to the development of
		  robust medical imaging models through its ability to learn
		  useful insights from copious medical datasets without
		  labels. In this review, we provide consistent descriptions
		  of different self-supervised learning strategies and
		  compose a systematic review of papers published between
		  2012 and 2022 on PubMed, Scopus, and ArXiv that applied
		  self-supervised learning to medical imaging classification.
		  We screened a total of 412 relevant studies and included 79
		  papers for data extraction and analysis. With this
		  comprehensive effort, we synthesize the collective
		  knowledge of prior work and provide implementation
		  guidelines for future researchers interested in applying
		  self-supervised learning to their development of medical
		  imaging classification models.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {npj Digital Medicine},
  author	= {Huang, Shih-Cheng and Pareek, Anuj and Jensen, Malte and
		  Lungren, Matthew P. and Yeung, Serena and Chaudhari, Akshay
		  S.},
  month		= apr,
  year		= {2023},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Medical imaging, Computer science, Scientific data},
  pages		= {74}
}

@Article{	  isensee2020,
  title		= {nnU-Net: a self-configuring method for deep learning-based
		  biomedical image segmentation},
  volume	= {18},
  issn		= {1548-7105},
  url		= {http://dx.doi.org/10.1038/s41592-020-01008-z},
  doi		= {10.1038/s41592-020-01008-z},
  number	= {2},
  journal	= {Nature Methods},
  publisher	= {Springer Science and Business Media LLC},
  author	= {Isensee, Fabian and Jaeger, Paul F. and Kohl, Simon A. A.
		  and Petersen, Jens and Maier-Hein, Klaus H.},
  year		= {2020},
  month		= dec,
  pages		= {203–211}
}

@InProceedings{	  janiva_temporal_miccai2025,
  author	= { Janíčková, Ivana AND Tan, Yen Y. AND Helbich, Thomas
		  H. AND Miloserdov, Konstantin AND Bago-Horvath, Zsuzsanna
		  AND Heber, Ulrike AND Langs, Georg},
  title		= { { Temporal Representation Learning of Phenotype
		  Trajectories for pCR Prediction in Breast Cancer } },
  booktitle	= {proceedings of Medical Image Computing and Computer
		  Assisted Intervention -- MICCAI 2025},
  year		= {2025},
  publisher	= {Springer Nature Switzerland},
  volume	= {LNCS 15974},
  month		= {September},
  page		= {606 -- 615}
}

@Book{		  jaynes_2003,
  place		= {Cambridge},
  title		= {Probability Theory: The Logic of Science},
  publisher	= {Cambridge University Press},
  author	= {Jaynes, E. T.},
  editor	= {Bretthorst, G. LarryEditor},
  year		= {2003}
}

@Article{	  jonkers2025102165,
  title		= {landmarker: A Toolkit for Anatomical Landmark Localization
		  in 2D/3D Images},
  journal	= {SoftwareX},
  volume	= {30},
  pages		= {102165},
  year		= {2025},
  issn		= {2352-7110},
  doi		= {https://doi.org/10.1016/j.softx.2025.102165},
  url		= {https://www.sciencedirect.com/science/article/pii/S2352711025001323},
  author	= {Jef Jonkers and Luc Duchateau and Glenn {Van Wallendael}
		  and Sofie {Van Hoecke}},
  keywords	= {Landmark localization, Keypoint detection, Medical image
		  analysis, Python, PyTorch},
  abstract	= {Anatomical landmark localization in 2D/3D images is a
		  critical task in medical imaging. Although many
		  general-purpose tools exist for landmark localization in
		  classical computer vision tasks, such as pose estimation,
		  they lack the specialized features and modularity necessary
		  for anatomical landmark localization applications in the
		  medical domain. Therefore, we introduce landmarker, a
		  Python package built on PyTorch. The package provides a
		  comprehensive, flexible toolkit for developing and
		  evaluating landmark localization algorithms, supporting a
		  range of methodologies, including static and adaptive
		  heatmap regression. landmarker enhances the accuracy of
		  landmark identification, streamlines research and
		  development processes, and supports various image formats
		  and preprocessing pipelines. Its modular design allows
		  users to customize and extend the toolkit for specific
		  datasets and applications, accelerating innovation in
		  medical imaging. landmarker addresses a critical need for
		  precision and customization in landmark localization tasks
		  not adequately met by existing general-purpose pose
		  estimation tools.}
}

@InBook{	  kamran:miccai:2025:lessiontime,
  author	= {Kamran, Mohammed and Bernathova, Maria and Varga, Raoul
		  and Singer, Christian and Bago-Horvath, Zsuzsanna and
		  Helbich, Thomas and Langs, Georg and Seeböck, Philipp},
  year		= {2025},
  month		= {09},
  pages		= {329-339},
  title		= {LesiOnTime - Joint Temporal and Clinical Modeling for
		  Small Breast Lesion Segmentation in Longitudinal DCE-MRI},
  isbn		= {978-3-032-05558-3},
  doi		= {10.1007/978-3-032-05559-0_33},
  publisher	= {Springer, Cham}
}

@Article{	  katzman2018-deepsurv,
  title		= {DeepSurv: personalized treatment recommender system using
		  a Cox proportional hazards deep neural network},
  volume	= {18},
  issn		= {1471-2288},
  url		= {http://dx.doi.org/10.1186/s12874-018-0482-1},
  doi		= {10.1186/s12874-018-0482-1},
  number	= {1},
  journal	= {BMC Medical Research Methodology},
  publisher	= {Springer Science and Business Media LLC},
  author	= {Katzman, Jared L. and Shaham, Uri and Cloninger, Alexander
		  and Bates, Jonathan and Jiang, Tingting and Kluger, Yuval},
  year		= {2018},
  month		= feb
}

@InProceedings{	  kim:midl2023:learning,
  title		= { Learning to Compare Longitudinal Images },
  author	= { Heejong Kim and Mert R. Sabuncu },
  booktitle	= { Medical Imaging with Deep Learning },
  year		= { 2023 },
  url		= { https://openreview.net/forum?id=l17YFzXLP53 }
}

@Article{	  koo2016icc,
  author	= {Koo, Terry K. and Li, Mae Y.},
  title		= {A Guideline of Selecting and Reporting Intraclass
		  Correlation Coefficients for Reliability Research},
  journal	= {Journal of Chiropractic Medicine},
  volume	= {15},
  number	= {2},
  pages		= {155--163},
  year		= {2016},
  month		= jun,
  publisher	= {Elsevier},
  doi		= {10.1016/j.jcm.2016.02.012},
  url		= {https://doi.org/10.1016/j.jcm.2016.02.012},
  issn		= {1556-3707}
}

@Article{	  kvamme-2019-pycox,
  author	= {H{{\aa}}vard Kvamme and {{\O}}rnulf Borgan and Ida Scheel},
  title		= {Time-to-Event Prediction with Neural Networks and Cox
		  Regression},
  journal	= {Journal of Machine Learning Research},
  year		= {2019},
  volume	= {20},
  number	= {129},
  pages		= {1--30},
  url		= {http://jmlr.org/papers/v20/18-424.html}
}

@Book{		  lamport:book:1989,
  author	= {Lamport, Leslie},
  title		= {Latex: A Document Preparation System},
  year		= {1986},
  isbn		= {0-201-15790-X},
  publisher	= {Addison-Wesley Longman Publishing Co., Inc.},
  address	= {Boston, MA, USA}
}

@InProceedings{	  lee2018deephit,
  title		= {Deephit: A deep learning approach to survival analysis
		  with competing risks},
  author	= {Lee, Changhee and Zame, William and Yoon, Jinsung and Van
		  Der Schaar, Mihaela},
  booktitle	= {Proceedings of the AAAI conference on artificial
		  intelligence},
  volume	= {32},
  number	= {1},
  year		= {2018}
}

@Misc{		  lin2018focallossdenseobject,
  title		= {Focal Loss for Dense Object Detection},
  author	= {Tsung-Yi Lin and Priya Goyal and Ross Girshick and Kaiming
		  He and Piotr Dollár},
  year		= {2018},
  eprint	= {1708.02002},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/1708.02002}
}

@InProceedings{	  ling2024_ieee_selfsupervisedlearningforra,
  author	= {Ling, Deyu and Yu, Wenxin and Zhang, Zhiqiang and Zou,
		  Jinmei},
  booktitle	= {2024 IEEE International Symposium on Circuits and Systems
		  (ISCAS)},
  title		= {An Attention Network With Self-Supervised Learning for
		  Rheumatoid Arthritis Scoring},
  year		= {2024},
  volume	= {},
  number	= {},
  pages		= {1-5},
  keywords	= {Training;Pathology;Supervised learning;Self-supervised
		  learning;Predictive models;Arthritis;Integrated circuit
		  modeling;Rheumatoid arthritis;Hand X-ray images;Deep
		  learning;Attention mechanisms;Self-supervised learning},
  doi		= {10.1109/ISCAS58744.2024.10558434}
}

@InProceedings{	  ling_attention_2024,
  title		= {An {Attention} {Network} {With} {Self}-{Supervised}
		  {Learning} for {Rheumatoid} {Arthritis} {Scoring}},
  url		= {https://ieeexplore.ieee.org/document/10558434},
  doi		= {10.1109/ISCAS58744.2024.10558434},
  abstract	= {Rheumatoid arthritis (RA) is a chronic disease causing
		  joint pain and disability. Early treatment can prevent
		  irreversible bone damage. The Sharp/van der Heijde (SvH)
		  method, a common clinical standard for assessing RA
		  progression, is manual and subjective, leading to
		  inconsistent measurements. To overcome this, we propose a
		  deep learning model based on the SvH scoring criteria to
		  predict SvH scores of RA patients’ hands. Our efficient
		  attention network, ECBANet, merges supervised and
		  self-supervised learning to generate attentional weights
		  without losing feature dimensionality. It effectively
		  captures the local features of the RA hand and enhances the
		  model’s focus on crucial cues like joint position and
		  gap. Extensive experiments on the only public dataset of
		  hand X-ray images of RA patients labeled with SvH scores
		  show that our model surpasses the current optimal model for
		  this dataset, reducing MAE by 2\% and RMSE by 7.4\%.},
  urldate	= {2025-10-09},
  booktitle	= {2024 {IEEE} {International} {Symposium} on {Circuits} and
		  {Systems} ({ISCAS})},
  author	= {Ling, Deyu and Yu, Wenxin and Zhang, Zhiqiang and Zou,
		  Jinmei},
  month		= may,
  year		= {2024},
  note		= {ISSN: 2158-1525},
  keywords	= {Deep learning, Training, Rheumatoid arthritis, Arthritis,
		  Attention mechanisms, Hand X-ray images, Integrated circuit
		  modeling, Pathology, Predictive models, Self-supervised
		  learning, Supervised learning},
  pages		= {1--5}
}

@Misc{		  liu2019endtoendmultitasklearningattention,
  title		= {End-to-End Multi-Task Learning with Attention},
  author	= {Shikun Liu and Edward Johns and Andrew J. Davison},
  year		= {2019},
  eprint	= {1803.10704},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/1803.10704}
}

@Misc{		  liu2025imageflownetforecastingmultiscaleimagelevel,
  title		= {ImageFlowNet: Forecasting Multiscale Image-Level
		  Trajectories of Disease Progression with
		  Irregularly-Sampled Longitudinal Medical Images},
  author	= {Chen Liu and Ke Xu and Liangbo L. Shen and Guillaume
		  Huguet and Zilong Wang and Alexander Tong and Danilo Bzdok
		  and Jay Stewart and Jay C. Wang and Lucian V. Del Priore
		  and Smita Krishnaswamy},
  year		= {2025},
  eprint	= {2406.14794},
  archiveprefix	= {arXiv},
  primaryclass	= {eess.IV},
  url		= {https://arxiv.org/abs/2406.14794}
}

@Misc{		  liu_imageflownet_2025,
  title		= {{ImageFlowNet}: {Forecasting} {Multiscale} {Image}-{Level}
		  {Trajectories} of {Disease} {Progression} with
		  {Irregularly}-{Sampled} {Longitudinal} {Medical} {Images}},
  shorttitle	= {{ImageFlowNet}},
  url		= {http://arxiv.org/abs/2406.14794},
  doi		= {10.48550/arXiv.2406.14794},
  abstract	= {Advances in medical imaging technologies have enabled the
		  collection of longitudinal images, which involve repeated
		  scanning of the same patients over time, to monitor disease
		  progression. However, predictive modeling of such data
		  remains challenging due to high dimensionality, irregular
		  sampling, and data sparsity. To address these issues, we
		  propose ImageFlowNet, a novel model designed to forecast
		  disease trajectories from initial images while preserving
		  spatial details. ImageFlowNet first learns multiscale joint
		  representation spaces across patients and time points, then
		  optimizes deterministic or stochastic flow fields within
		  these spaces using a position-parameterized neural ODE/SDE
		  framework. The model leverages a UNet architecture to
		  create robust multiscale representations and mitigates data
		  scarcity by combining knowledge from all patients. We
		  provide theoretical insights that support our formulation
		  of ODEs, and motivate our regularizations involving
		  high-level visual features, latent space organization, and
		  trajectory smoothness. We validate ImageFlowNet on three
		  longitudinal medical image datasets depicting progression
		  in geographic atrophy, multiple sclerosis, and
		  glioblastoma, demonstrating its ability to effectively
		  forecast disease progression and outperform existing
		  methods. Our contributions include the development of
		  ImageFlowNet, its theoretical underpinnings, and empirical
		  validation on real-world datasets. The official
		  implementation is available at
		  https://github.com/KrishnaswamyLab/ImageFlowNet.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Liu, Chen and Xu, Ke and Shen, Liangbo L. and Huguet,
		  Guillaume and Wang, Zilong and Tong, Alexander and Bzdok,
		  Danilo and Stewart, Jay and Wang, Jay C. and Priore, Lucian
		  V. Del and Krishnaswamy, Smita},
  month		= apr,
  year		= {2025},
  note		= {arXiv:2406.14794 [eess]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning,
		  Electrical Engineering and Systems Science - Image and
		  Video Processing}
}

@Article{	  liu_self-supervised_2023,
  title		= {Self-{Supervised} {Contrastive} {Learning} for {Medical}
		  {Time} {Series}: {A} {Systematic} {Review}},
  volume	= {23},
  issn		= {1424-8220},
  shorttitle	= {Self-{Supervised} {Contrastive} {Learning} for {Medical}
		  {Time} {Series}},
  doi		= {10.3390/s23094221},
  abstract	= {Medical time series are sequential data collected over
		  time that measures health-related signals, such as
		  electroencephalography (EEG), electrocardiography (ECG),
		  and intensive care unit (ICU) readings. Analyzing medical
		  time series and identifying the latent patterns and trends
		  that lead to uncovering highly valuable insights for
		  enhancing diagnosis, treatment, risk assessment, and
		  disease progression. However, data mining in medical time
		  series is heavily limited by the sample annotation which is
		  time-consuming and labor-intensive, and expert-depending.
		  To mitigate this challenge, the emerging self-supervised
		  contrastive learning, which has shown great success since
		  2020, is a promising solution. Contrastive learning aims to
		  learn representative embeddings by contrasting positive and
		  negative samples without the requirement for explicit
		  labels. Here, we conducted a systematic review of how
		  contrastive learning alleviates the label scarcity in
		  medical time series based on PRISMA standards. We searched
		  the studies in five scientific databases (IEEE, ACM,
		  Scopus, Google Scholar, and PubMed) and retrieved 1908
		  papers based on the inclusion criteria. After applying
		  excluding criteria, and screening at title, abstract, and
		  full text levels, we carefully reviewed 43 papers in this
		  area. Specifically, this paper outlines the pipeline of
		  contrastive learning, including pre-training, fine-tuning,
		  and testing. We provide a comprehensive summary of the
		  various augmentations applied to medical time series data,
		  the architectures of pre-training encoders, the types of
		  fine-tuning classifiers and clusters, and the popular
		  contrastive loss functions. Moreover, we present an
		  overview of the different data types used in medical time
		  series, highlight the medical applications of interest, and
		  provide a comprehensive table of 51 public datasets that
		  have been utilized in this field. In addition, this paper
		  will provide a discussion on the promising future scopes
		  such as providing guidance for effective augmentation
		  design, developing a unified framework for analyzing
		  hierarchical time series, and investigating methods for
		  processing multimodal data. Despite being in its early
		  stages, self-supervised contrastive learning has shown
		  great potential in overcoming the need for expert-created
		  annotations in the research of medical time series.},
  language	= {eng},
  number	= {9},
  journal	= {Sensors (Basel, Switzerland)},
  author	= {Liu, Ziyu and Alavi, Azadeh and Li, Minyi and Zhang,
		  Xiang},
  month		= apr,
  year		= {2023},
  pmid		= {37177423},
  pmcid		= {PMC10181273},
  keywords	= {contrastive learning, Data Mining, Databases, Factual,
		  deep learning, healthcare, Learning, medical time series,
		  pretext tasks, self-supervised learning, systematic review,
		  Time Factors},
  pages		= {4221}
}

@Article{	  ma_segment_2024,
  title		= {Segment anything in medical images},
  volume	= {15},
  copyright	= {2024 The Author(s)},
  issn		= {2041-1723},
  url		= {https://www.nature.com/articles/s41467-024-44824-z},
  doi		= {10.1038/s41467-024-44824-z},
  abstract	= {Medical image segmentation is a critical component in
		  clinical practice, facilitating accurate diagnosis,
		  treatment planning, and disease monitoring. However,
		  existing methods, often tailored to specific modalities or
		  disease types, lack generalizability across the diverse
		  spectrum of medical image segmentation tasks. Here we
		  present MedSAM, a foundation model designed for bridging
		  this gap by enabling universal medical image segmentation.
		  The model is developed on a large-scale medical image
		  dataset with 1,570,263 image-mask pairs, covering 10
		  imaging modalities and over 30 cancer types. We conduct a
		  comprehensive evaluation on 86 internal validation tasks
		  and 60 external validation tasks, demonstrating better
		  accuracy and robustness than modality-wise specialist
		  models. By delivering accurate and efficient segmentation
		  across a wide spectrum of tasks, MedSAM holds significant
		  potential to expedite the evolution of diagnostic tools and
		  the personalization of treatment plans.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {Nature Communications},
  author	= {Ma, Jun and He, Yuting and Li, Feifei and Han, Lin and
		  You, Chenyu and Wang, Bo},
  month		= jan,
  year		= {2024},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Medical imaging, Anatomy, Computer science, Machine
		  learning},
  pages		= {654}
}

@Article{	  matsuo_machine_2022,
  title		= {Machine learning-based prediction of relapse in rheumatoid
		  arthritis patients using data on ultrasound examination and
		  blood test},
  volume	= {12},
  copyright	= {2022 The Author(s)},
  issn		= {2045-2322},
  url		= {https://www.nature.com/articles/s41598-022-11361-y},
  doi		= {10.1038/s41598-022-11361-y},
  abstract	= {Recent effective therapies enable most rheumatoid
		  arthritis (RA) patients to achieve remission; however, some
		  patients experience relapse. We aimed to predict relapse in
		  RA patients through machine learning (ML) using data on
		  ultrasound (US) examination and blood test. Overall, 210
		  patients with RA in remission at baseline were dichotomized
		  into remission (n = 150) and relapse (n = 60) based
		  on the disease activity at 2-year follow-up. Three ML
		  classifiers [Logistic Regression, Random Forest, and
		  extreme gradient boosting (XGBoost)] and data on 73
		  features (14 US examination data, 54 blood test data, and
		  five data on patient information) at baseline were used for
		  predicting relapse. The best performance was obtained using
		  the XGBoost classifier (area under the receiver operator
		  characteristic curve (AUC) = 0.747), compared with
		  Random Forest and Logistic Regression (AUC = 0.719 and
		  0.701, respectively). In the XGBoost classifier prediction,
		  ten important features, including wrist/metatarsophalangeal
		  superb microvascular imaging scores, were selected using
		  the recursive feature elimination method. The performance
		  was superior to that predicted by researcher-selected
		  features, which are conventional prognostic markers. These
		  results suggest that ML can provide an accurate prediction
		  of relapse in RA patients, and the use of predictive
		  algorithms may facilitate personalized treatment options.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-09},
  journal	= {Scientific Reports},
  author	= {Matsuo, Hidemasa and Kamada, Mayumi and Imamura, Akari and
		  Shimizu, Madoka and Inagaki, Maiko and Tsuji, Yuko and
		  Hashimoto, Motomu and Tanaka, Masao and Ito, Hiromu and
		  Fujii, Yasutomo},
  month		= may,
  year		= {2022},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Machine learning, Rheumatoid arthritis, Ultrasound},
  pages		= {7224}
}

@Misc{		  maziarz2022deeplearningrheumatoidarthritis,
  title		= {Deep Learning for Rheumatoid Arthritis: Joint Detection
		  and Damage Scoring in X-rays},
  author	= {Krzysztof Maziarz and Anna Krason and Zbigniew Wojna},
  year		= {2022},
  eprint	= {2104.13915},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/2104.13915}
}

@Misc{		  maziarz_deep_2022,
  title		= {Deep {Learning} for {Rheumatoid} {Arthritis}: {Joint}
		  {Detection} and {Damage} {Scoring} in {X}-rays},
  shorttitle	= {Deep {Learning} for {Rheumatoid} {Arthritis}},
  url		= {http://arxiv.org/abs/2104.13915},
  doi		= {10.48550/arXiv.2104.13915},
  abstract	= {Recent advancements in computer vision promise to automate
		  medical image analysis. Rheumatoid arthritis is an
		  autoimmune disease that would profit from computer-based
		  diagnosis, as there are no direct markers known, and
		  doctors have to rely on manual inspection of X-ray images.
		  In this work, we present a multi-task deep learning model
		  that simultaneously learns to localize joints on X-ray
		  images and diagnose two kinds of joint damage: narrowing
		  and erosion. Additionally, we propose a modification of
		  label smoothing, which combines classification and
		  regression cues into a single loss and achieves 5\%
		  relative error reduction compared to standard loss
		  functions. Our final model obtained 4th place in joint
		  space narrowing and 5th place in joint erosion in the
		  global RA2 DREAM challenge.},
  urldate	= {2025-10-09},
  publisher	= {arXiv},
  author	= {Maziarz, Krzysztof and Krason, Anna and Wojna, Zbigniew},
  month		= nov,
  year		= {2022},
  note		= {arXiv:2104.13915 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning}
}

@Misc{		  misc:ra2dreamchallenge:winner,
  title		= {RA2-DREAM Challenge: Winning Solution},
  author	= {DREAM RA2 Challenge Team},
  year		= {2020},
  howpublished	= {\url{https://www.synapse.org/Synapse:syn21680642/wiki/604549}},
  note		= {Accessed: 2025-12-05}
}

@Article{	  moon_deep_2025,
  title		= {Deep learning analysis for rheumatologic imaging: current
		  trends, future directions, and the role of human},
  volume	= {32},
  shorttitle	= {Deep learning analysis for rheumatologic imaging},
  url		= {https://www.jrd.or.kr/journal/view.html?doi=10.4078/jrd.2024.0128},
  doi		= {10.4078/jrd.2024.0128},
  abstract	= {Jucheol Moon, Ph.D., Pratik Jadhav, M.S., Sangtae Choi,
		  M.D., Ph.D.. J Rheum Dis 2025;32:73-88.
		  https://doi.org/10.4078/jrd.2024.0128},
  language	= {en},
  number	= {2},
  urldate	= {2025-10-07},
  journal	= {Journal of Rheumatic Diseases},
  author	= {Moon, Jucheol and Jadhav, Pratik and Choi, Sangtae},
  month		= apr,
  year		= {2025},
  note		= {Publisher: Korean College of Rheumatology},
  pages		= {73--88}
}

@Article{	  moon_deep_2025-1,
  title		= {Deep learning analysis for rheumatologic imaging: current
		  trends, future directions, and the role of human},
  volume	= {32},
  shorttitle	= {Deep learning analysis for rheumatologic imaging},
  url		= {https://www.jrd.or.kr/journal/view.html?doi=10.4078/jrd.2024.0128},
  doi		= {10.4078/jrd.2024.0128},
  abstract	= {Jucheol Moon, Ph.D., Pratik Jadhav, M.S., Sangtae Choi,
		  M.D., Ph.D.. J Rheum Dis 2025;32:73-88.
		  https://doi.org/10.4078/jrd.2024.0128},
  language	= {en},
  number	= {2},
  urldate	= {2025-10-09},
  journal	= {Journal of Rheumatic Diseases},
  author	= {Moon, Jucheol and Jadhav, Pratik and Choi, Sangtae},
  month		= apr,
  year		= {2025},
  note		= {Publisher: Korean College of Rheumatology},
  pages		= {73--88}
}

@Article{	  moor_foundation_2023,
  title		= {Foundation models for generalist medical artificial
		  intelligence},
  volume	= {616},
  copyright	= {2023 Springer Nature Limited},
  issn		= {1476-4687},
  url		= {https://www.nature.com/articles/s41586-023-05881-4},
  doi		= {10.1038/s41586-023-05881-4},
  abstract	= {The exceptionally rapid development of highly flexible,
		  reusable artificial intelligence (AI) models is likely to
		  usher in newfound capabilities in medicine. We propose a
		  new paradigm for medical AI, which we refer to as
		  generalist medical AI (GMAI). GMAI models will be capable
		  of carrying out a diverse set of tasks using very little or
		  no task-specific labelled data. Built through
		  self-supervision on large, diverse datasets, GMAI will
		  flexibly interpret different combinations of medical
		  modalities, including data from imaging, electronic health
		  records, laboratory results, genomics, graphs or medical
		  text. Models will in turn produce expressive outputs such
		  as free-text explanations, spoken recommendations or image
		  annotations that demonstrate advanced medical reasoning
		  abilities. Here we identify a set of high-impact potential
		  applications for GMAI and lay out specific technical
		  capabilities and training datasets necessary to enable
		  them. We expect that GMAI-enabled applications will
		  challenge current strategies for regulating and validating
		  AI devices for medicine and will shift practices associated
		  with the collection of large medical datasets.},
  language	= {en},
  number	= {7956},
  urldate	= {2025-10-07},
  journal	= {Nature},
  author	= {Moor, Michael and Banerjee, Oishi and Abad, Zahra Shakeri
		  Hossein and Krumholz, Harlan M. and Leskovec, Jure and
		  Topol, Eric J. and Rajpurkar, Pranav},
  month		= apr,
  year		= {2023},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Computational biology and bioinformatics, Health care},
  pages		= {259--265}
}

@Article{	  moradmand2025,
  title		= {Multistage deep learning methods for automating
		  radiographic sharp score prediction in rheumatoid
		  arthritis},
  volume	= {15},
  issn		= {2045-2322},
  url		= {http://dx.doi.org/10.1038/s41598-025-86073-0},
  doi		= {10.1038/s41598-025-86073-0},
  number	= {1},
  journal	= {Scientific Reports},
  publisher	= {Springer Science and Business Media LLC},
  author	= {Moradmand, Hajar and Ren, Lei},
  year		= {2025},
  month		= jan
}

@Article{	  moradmand_multistage_2025,
  title		= {Multistage deep learning methods for automating
		  radiographic sharp score prediction in rheumatoid
		  arthritis},
  volume	= {15},
  copyright	= {2025 The Author(s)},
  issn		= {2045-2322},
  url		= {https://www.nature.com/articles/s41598-025-86073-0},
  doi		= {10.1038/s41598-025-86073-0},
  abstract	= {The Sharp-van der Heijde score (SvH) is crucial for
		  assessing joint damage in rheumatoid arthritis (RA) through
		  radiographic images. However, manual scoring is
		  time-consuming and subject to variability. This study
		  proposes a multistage deep learning model to predict the
		  Overall Sharp Score (OSS) from hand X-ray images. The
		  framework involves four stages: image preprocessing, hand
		  segmentation with UNet, joint identification via YOLOv7,
		  and OSS prediction utilizing a custom Vision Transformer
		  (ViT). Evaluation metrics included Intersection over Union
		  (IoU), Mean Absolute Error (MAE), Root Mean Squared Error
		  (RMSE), Huber loss, and Intraclass Correlation Coefficient
		  (ICC). The model was trained using stratified group 3-fold
		  cross-validation on a dataset of 679 patients and tested
		  externally on 291 subjects. The joint identification model
		  achieved 99\% accuracy. The ViT model achieved the best OSS
		  prediction for patients with Sharp
		  scores {\textless} 50. It achieved a Huber loss of 4.9,
		  an RMSE of 9.73, and an MAE of 5.35, demonstrating a strong
		  correlation with expert scores (ICC = 0.702,
		  P {\textless} 0.001). This study is the first to apply
		  a ViT for OSS prediction in RA. It presents an efficient
		  and automated alternative for overall damage assessment.
		  This approach may reduce reliance on manual scoring.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {Scientific Reports},
  author	= {Moradmand, Hajar and Ren, Lei},
  month		= jan,
  year		= {2025},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Health care, Medical research, Rheumatology},
  pages		= {3391}
}

@Article{	  morano_mirage_2025,
  title		= {{MIRAGE}: {Multimodal} foundation model and benchmark for
		  comprehensive retinal {OCT} image analysis},
  volume	= {8},
  issn		= {2398-6352},
  shorttitle	= {{MIRAGE}},
  url		= {http://arxiv.org/abs/2506.08900},
  doi		= {10.1038/s41746-025-01852-3},
  abstract	= {Artificial intelligence (AI) has become a fundamental tool
		  for assisting clinicians in analyzing ophthalmic images,
		  such as optical coherence tomography (OCT). However,
		  developing AI models often requires extensive annotation,
		  and existing models tend to underperform on independent,
		  unseen data. Foundation models (FMs), large AI models
		  trained on vast unlabeled datasets, have shown promise in
		  overcoming these challenges. Nonetheless, available FMs for
		  ophthalmology lack extensive validation, especially for
		  segmentation tasks, and focus on a single imaging modality.
		  In this context, we propose MIRAGE, a novel multimodal FM
		  for the analysis of OCT and scanning laser ophthalmoscopy
		  (SLO) images. Additionally, we propose a new evaluation
		  benchmark with OCT/SLO classification and segmentation
		  tasks. The comparison with general and specialized FMs and
		  segmentation methods shows the superiority of MIRAGE in
		  both types of tasks, highlighting its suitability as a
		  basis for the development of robust AI systems for retinal
		  OCT image analysis. Both MIRAGE and the evaluation
		  benchmark are publicly available:
		  https://github.com/j-morano/MIRAGE.},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {npj Digital Medicine},
  author	= {Morano, José and Fazekas, Botond and Sükei, Emese and
		  Fecso, Ronald and Emre, Taha and Gumpinger, Markus and
		  Faustmann, Georg and Oghbaie, Marzieh and Schmidt-Erfurth,
		  Ursula and Bogunović, Hrvoje},
  month		= sep,
  year		= {2025},
  note		= {arXiv:2506.08900 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition},
  pages		= {576}
}

@Article{	  morano_multimodal_2025,
  title		= {Multimodal foundation model and benchmark for
		  comprehensive retinal {OCT} image analysis},
  volume	= {8},
  copyright	= {2025 The Author(s)},
  issn		= {2398-6352},
  url		= {https://www.nature.com/articles/s41746-025-01852-3},
  doi		= {10.1038/s41746-025-01852-3},
  abstract	= {Artificial intelligence (AI) has become a fundamental tool
		  for assisting clinicians in analyzing ophthalmic images,
		  such as optical coherence tomography (OCT). However,
		  developing AI models often requires extensive annotation,
		  and existing models tend to underperform on independent,
		  unseen data. Foundation models (FMs), large AI models
		  trained on vast, unlabeled datasets, have shown promise in
		  overcoming these challenges. Nonetheless, available FMs for
		  ophthalmology lack extensive validation, especially for
		  segmentation tasks, and focus on a single imaging modality.
		  In this context, we propose MIRAGE, a novel multimodal FM
		  for the analysis of OCT and scanning laser ophthalmoscopy
		  (SLO) images. Additionally, we propose a new evaluation
		  benchmark with OCT/SLO classification and segmentation
		  tasks. The comparison with general and specialized FMs and
		  segmentation methods shows the superiority of MIRAGE in
		  both types of tasks, highlighting its suitability as a
		  basis for the development of robust AI systems for retinal
		  OCT image analysis.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {npj Digital Medicine},
  author	= {Morano, José and Fazekas, Botond and Sükei, Emese and
		  Fecso, Ronald and Emre, Taha and Gumpinger, Markus and
		  Faustmann, Georg and Oghbaie, Marzieh and Schmidt-Erfurth,
		  Ursula and Bogunović, Hrvoje},
  month		= sep,
  year		= {2025},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Diagnosis, Eye diseases, Medical imaging},
  pages		= {576}
}

@Misc{		  nath_vila-m3_2025,
  title		= {{VILA}-{M3}: {Enhancing} {Vision}-{Language} {Models} with
		  {Medical} {Expert} {Knowledge}},
  shorttitle	= {{VILA}-{M3}},
  url		= {http://arxiv.org/abs/2411.12915},
  doi		= {10.48550/arXiv.2411.12915},
  abstract	= {Generalist vision language models (VLMs) have made
		  significant strides in computer vision, but they fall short
		  in specialized fields like healthcare, where expert
		  knowledge is essential. In traditional computer vision
		  tasks, creative or approximate answers may be acceptable,
		  but in healthcare, precision is paramount.Current large
		  multimodal models like Gemini and GPT-4o are insufficient
		  for medical tasks due to their reliance on memorized
		  internet knowledge rather than the nuanced expertise
		  required in healthcare. VLMs are usually trained in three
		  stages: vision pre-training, vision-language pre-training,
		  and instruction fine-tuning (IFT). IFT has been typically
		  applied using a mixture of generic and healthcare data. In
		  contrast, we propose that for medical VLMs, a fourth stage
		  of specialized IFT is necessary, which focuses on medical
		  data and includes information from domain expert models.
		  Domain expert models developed for medical use are crucial
		  because they are specifically trained for certain clinical
		  tasks, e.g. to detect tumors and classify abnormalities
		  through segmentation and classification, which learn
		  fine-grained features of medical data\$-\$features that are
		  often too intricate for a VLM to capture effectively
		  especially in radiology. This paper introduces a new
		  framework, VILA-M3, for medical VLMs that utilizes domain
		  knowledge via expert models. Through our experiments, we
		  show an improved state-of-the-art (SOTA) performance with
		  an average improvement of {\textasciitilde}9\% over the
		  prior SOTA model Med-Gemini and {\textasciitilde}6\% over
		  models trained on the specific tasks. Our approach
		  emphasizes the importance of domain expertise in creating
		  precise, reliable VLMs for medical applications.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Nath, Vishwesh and Li, Wenqi and Yang, Dong and Myronenko,
		  Andriy and Zheng, Mingxin and Lu, Yao and Liu, Zhijian and
		  Yin, Hongxu and Tang, Yucheng and Guo, Pengfei and Zhao,
		  Can and Xu, Ziyue and He, Yufan and Heinrich, Greg and Law,
		  Yee Man and Simon, Benjamin and Harmon, Stephanie and
		  Aylward, Stephen and Edgar, Marc and Zephyr, Michael and
		  Han, Song and Molchanov, Pavlo and Turkbey, Baris and Roth,
		  Holger and Xu, Daguang},
  month		= mar,
  year		= {2025},
  note		= {arXiv:2411.12915 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition}
}

@InProceedings{	  nips2011_bergstra_algorithmsforhyperparameteroptimization,
  author	= {Bergstra, James and Bardenet, R\'{e}mi and Bengio, Yoshua
		  and K\'{e}gl, Bal\'{a}zs},
  booktitle	= {Advances in Neural Information Processing Systems},
  editor	= {J. Shawe-Taylor and R. Zemel and P. Bartlett and F.
		  Pereira and K.Q. Weinberger},
  pages		= {},
  publisher	= {Curran Associates, Inc.},
  title		= {Algorithms for Hyper-Parameter Optimization},
  url		= {https://proceedings.neurips.cc/paper_files/paper/2011/file/86e8f7ab32cfd12577bc2619bc635690-Paper.pdf},
  volume	= {24},
  year		= {2011}
}

@InProceedings{	  obadic2024:cvpr:contrastive_pretraining_for_visual_concept_explanations_of_socioeconomic_outcomes_using_rnc,
  author	= {Obadic, Ivica and Levering, Alex and Pennig, Lars and
		  Oliveira, Dario and Marcos, Diego and Zhu, Xiaoxiang},
  booktitle	= {2024 IEEE/CVF Conference on Computer Vision and Pattern
		  Recognition Workshops (CVPRW)},
  title		= {Contrastive Pretraining for Visual Concept Explanations of
		  Socioeconomic Outcomes},
  year		= {2024},
  volume	= {},
  number	= {},
  pages		= {575-584},
  keywords	= {Deep
		  learning;Training;Visualization;Sensitivity;Vegetation
		  mapping;Predictive models;Vectors;post-hoc concept
		  explanations;socioeconomic-outcomes;contrastive-pretraining},
  doi		= {10.1109/CVPRW63382.2024.00062}
}

@Article{	  oquab2023dinov2,
  title		= {Dinov2: Learning robust visual features without
		  supervision},
  author	= {Oquab, Maxime and Darcet, Timoth{\'e}e and Moutakanni,
		  Th{\'e}o and Vo, Huy and Szafraniec, Marc and Khalidov,
		  Vasil and Fernandez, Pierre and Haziza, Daniel and Massa,
		  Francisco and El-Nouby, Alaaeldin and others},
  journal	= {arXiv preprint arXiv:2304.07193},
  year		= {2023}
}

@Article{	  parashar_medical_2023,
  title		= {Medical imaging in rheumatoid arthritis: {A} review on
		  deep learning approach},
  volume	= {18},
  issn		= {2391-5412},
  shorttitle	= {Medical imaging in rheumatoid arthritis},
  doi		= {10.1515/biol-2022-0611},
  abstract	= {Arthritis is a musculoskeletal disorder. Millions of
		  people have arthritis, making it one of the most common
		  joint disorders. Osteoarthritis (OA) and rheumatoid
		  arthritis (RA) are the most common types of arthritis among
		  the many different types available. Pain, stiffness, and
		  inflammation are among the early signs of arthritis, which
		  can progress to severe immobility at a later stage if left
		  untreated. Although arthritis cannot be cured at any point
		  in time, it can be managed if diagnosed and treated
		  correctly. Clinical diagnostic and medical imaging methods
		  are currently used to evaluate OA and RA, both debilitating
		  conditions. This review is focused on deep learning
		  approaches used by taking medical imaging (X-rays and
		  magnetic resonance imaging) as input for the detection of
		  RA.},
  language	= {eng},
  number	= {1},
  journal	= {Open Life Sciences},
  author	= {Parashar, Apoorva and Rishi, Rahul and Parashar, Anubha
		  and Rida, Imad},
  year		= {2023},
  pmid		= {37426615},
  pmcid		= {PMC10329279},
  keywords	= {deep learning, rheumatoid arthritis, arthritis, hand
		  rheumatoid arthritis, knee rheumatoid arthritis, medical
		  imaging},
  pages		= {20220611}
}

@Article{	  payer2019207,
  title		= {Integrating spatial configuration into heatmap regression
		  based CNNs for landmark localization},
  journal	= {Medical Image Analysis},
  volume	= {54},
  pages		= {207-219},
  year		= {2019},
  issn		= {1361-8415},
  doi		= {10.1016/j.media.2019.03.007},
  url		= {https://www.sciencedirect.com/science/article/pii/S1361841518305784},
  author	= {Christian Payer and Darko {\v{S}}tern and Horst Bischof
		  and Martin Urschler},
  keywords	= {Anatomical landmarks, Localization, Fully convolutional
		  networks, Heatmap regression},
  abstract	= {In many medical image analysis applications, only a
		  limited amount of training data is available due to the
		  costs of image acquisition and the large manual annotation
		  effort required from experts. Training recent
		  state-of-the-art machine learning methods like
		  convolutional neural networks (CNNs) from small datasets is
		  a challenging task. In this work on anatomical landmark
		  localization, we propose a CNN architecture that learns to
		  split the localization task into two simpler sub-problems,
		  reducing the overall need for large training datasets. Our
		  fully convolutional SpatialConfiguration-Net (SCN) learns
		  this simplification due to multiplying the heatmap
		  predictions of its two components and by training the
		  network in an end-to-end manner. Thus, the SCN dedicates
		  one component to locally accurate but ambiguous candidate
		  predictions, while the other component improves robustness
		  to ambiguities by incorporating the spatial configuration
		  of landmarks. In our extensive experimental evaluation, we
		  show that the proposed SCN outperforms related methods in
		  terms of landmark localization error on a variety of
		  size-limited 2D and 3D landmark localization datasets,
		  i.e., hand radiographs, lateral cephalograms, hand MRIs,
		  and spine CTs.}
}

@Article{	  perneger1998:what_is_wrong_with_bonferroni,
  author	= {Perneger, Thomas V},
  title		= {What{\textquoteright}s wrong with Bonferroni adjustments},
  volume	= {316},
  number	= {7139},
  pages		= {1236--1238},
  year		= {1998},
  doi		= {10.1136/bmj.316.7139.1236},
  publisher	= {BMJ Publishing Group Ltd},
  issn		= {0959-8138},
  url		= {https://www.bmj.com/content/316/7139/1236},
  eprint	= {https://www.bmj.com/content},
  journal	= {BMJ}
}

@Article{	  peterfy2011comparison,
  title		= {Comparison of the Genant-modified Sharp and van der
		  Heijde-modified Sharp scoring methods for radiographic
		  assessment in rheumatoid arthritis},
  author	= {Peterfy, Charles G and Wu, Chun and Lu, Ying and Genovese,
		  Mark},
  journal	= {International Journal of Clinical Rheumatology},
  volume	= {6},
  number	= {1},
  pages		= {15},
  year		= {2011},
  publisher	= {Future Medicine Ltd}
}

@Manual{	  r_language,
  title		= {R: A Language and Environment for Statistical Computing},
  author	= {{R Core Team}},
  organization	= {R Foundation for Statistical Computing},
  address	= {Vienna, Austria},
  year		= {2021},
  url		= {https://www.R-project.org/}
}

@Manual{	  r_package_psych2025,
  title		= {psych: Procedures for Psychological, Psychometric, and
		  Personality Research},
  author	= {{William Revelle}},
  organization	= {Northwestern University},
  address	= {Evanston, Illinois},
  year		= {2025},
  note		= {R package version 2.5.6},
  url		= {https://CRAN.R-project.org/package=psych}
}

@Article{	  rainio_evaluation_2024,
  title		= {Evaluation metrics and statistical tests for machine
		  learning},
  volume	= {14},
  issn		= {2045-2322},
  url		= {https://doi.org/10.1038/s41598-024-56706-x},
  doi		= {10.1038/s41598-024-56706-x},
  abstract	= {Research on different machine learning (ML) has become
		  incredibly popular during the past few decades. However,
		  for some researchers not familiar with statistics, it might
		  be difficult to understand how to evaluate the performance
		  of ML models and compare them with each other. Here, we
		  introduce the most common evaluation metrics used for the
		  typical supervised ML tasks including binary, multi-class,
		  and multi-label classification, regression, image
		  segmentation, object detection, and information retrieval.
		  We explain how to choose a suitable statistical test for
		  comparing models, how to obtain enough values of the metric
		  for testing, and how to perform the test and interpret its
		  results. We also present a few practical examples about
		  comparing convolutional neural networks used to classify
		  X-rays with different lung infections and detect cancer
		  tumors in positron emission tomography images.},
  number	= {1},
  journal	= {Scientific Reports},
  author	= {Rainio, Oona and Teuho, Jarmo and Klén, Riku},
  month		= mar,
  year		= {2024},
  pages		= {6086}
}

@Misc{		  rajendran2024learninginterpretableconceptsunifying,
  title		= {Learning Interpretable Concepts: Unifying Causal
		  Representation Learning and Foundation Models},
  author	= {Goutham Rajendran and Simon Buchholz and Bryon Aragam and
		  Bernhard Schölkopf and Pradeep Ravikumar},
  year		= {2024},
  eprint	= {2402.09236},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2402.09236}
}

@Misc{		  rajendran_learning_2024,
  title		= {Learning {Interpretable} {Concepts}: {Unifying} {Causal}
		  {Representation} {Learning} and {Foundation} {Models}},
  shorttitle	= {Learning {Interpretable} {Concepts}},
  url		= {http://arxiv.org/abs/2402.09236},
  doi		= {10.48550/arXiv.2402.09236},
  abstract	= {To build intelligent machine learning systems, there are
		  two broad approaches. One approach is to build inherently
		  interpretable models, as endeavored by the growing field of
		  causal representation learning. The other approach is to
		  build highly-performant foundation models and then invest
		  efforts into understanding how they work. In this work, we
		  relate these two approaches and study how to learn
		  human-interpretable concepts from data. Weaving together
		  ideas from both fields, we formally define a notion of
		  concepts and show that they can be provably recovered from
		  diverse data. Experiments on synthetic data and large
		  language models show the utility of our unified approach.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Rajendran, Goutham and Buchholz, Simon and Aragam, Bryon
		  and Schölkopf, Bernhard and Ravikumar, Pradeep},
  month		= dec,
  year		= {2024},
  note		= {arXiv:2402.09236 [cs]},
  keywords	= {Computer Science - Machine Learning, Computer Science -
		  Artificial Intelligence, Mathematics - Statistics Theory,
		  Statistics - Machine Learning, Statistics - Statistics
		  Theory}
}

@Article{	  rajesh_enhancing_nodate,
  title		= {Enhancing rheumatoid arthritis detection through
		  multimodal segmentation with {Regularized} {Parametric}
		  {ReLU} in deep networks},
  volume	= {0},
  issn		= {1448-837X},
  url		= {https://doi.org/10.1080/1448837X.2025.2553935},
  doi		= {10.1080/1448837X.2025.2553935},
  abstract	= {Rheumatoid arthritis (RA) is a chronic autoimmune disease
		  that, more often than not, impacts the joints. Early and
		  accurate detection of RA is critical for powerful treatment
		  and control of the disease. The Multimodal Segmentation and
		  Watershed Refinement (MMSWR) technique is proposed as a
		  unique method for fine segmentation in complex biological
		  images. Leveraging multimodal segmentation and refined
		  watershed techniques, MMSWR aims to gain higher accuracy
		  and robustness in delineating objects of interest. MMSWR
		  captures various image features to enhance segmentation
		  accuracy by combining intensity-based and edge-based
		  segmentation modalities. The Marker-Controlled Watershed
		  algorithm also refines segmentation by incorporating
		  previous expertise via markers. The technique integrates
		  spatial facts and adaptive thresholding to dynamically
		  determine local thresholds, ensuring unique segmentation
		  even in images with various systems. Moreover, including
		  Regularized Parametric ReLU (RP-ReLU) activations and Adam
		  optimisation in the proposed Deep Neural Network (DNN)
		  architecture enhances the accuracy (97\%) and loss of 0.01
		  without over fitting and local minima.},
  number	= {0},
  urldate	= {2025-10-09},
  journal	= {Australian Journal of Electrical and Electronics
		  Engineering},
  author	= {Rajesh, Govindan and Malarvizhi, Nandagopal},
  note		= {Publisher: EA Website \_eprint:
		  https://doi.org/10.1080/1448837X.2025.2553935},
  keywords	= {biological images, image processing, Marker-Controlled
		  Watershed, Multimodal segmentation, segmentation
		  modalities, watershed refinement},
  pages		= {1--20}
}

@Article{	  reinivs2023assessment,
  title		= {Assessment of portal hypertension severity using machine
		  learning models in patients with compensated cirrhosis},
  author	= {Reini{\v{s}}, Ji{\v{r}}{\'\i} and Petrenko, Oleksandr and
		  Simbrunner, Benedikt and Hofer, Benedikt S and Schepis,
		  Filippo and Scoppettuolo, Marco and Saltini, Dario and
		  Indulti, Federica and Guasconi, Tomas and Albillos, Agustin
		  and others},
  journal	= {Journal of Hepatology},
  volume	= {78},
  number	= {2},
  pages		= {390--400},
  year		= {2023},
  publisher	= {Elsevier}
}

@Article{	  ronneberger2015,
  author	= {Olaf Ronneberger and Philipp Fischer and Thomas Brox},
  title		= {U-Net: Convolutional Networks for Biomedical Image
		  Segmentation},
  journal	= {CoRR},
  volume	= {abs/1505.04597},
  year		= {2015},
  url		= {http://arxiv.org/abs/1505.04597},
  eprinttype	= {arXiv},
  eprint	= {1505.04597},
  timestamp	= {Mon, 13 Aug 2018 16:46:52 +0200},
  biburl	= {https://dblp.org/rec/journals/corr/RonnebergerFB15.bib},
  bibsource	= {dblp computer science bibliography, https://dblp.org}
}

@InBook{	  roschewitz_2024,
  title		= {Counterfactual Contrastive Learning: Robust
		  Representations via Causal Image Synthesis},
  isbn		= {9783031737480},
  issn		= {1611-3349},
  url		= {http://dx.doi.org/10.1007/978-3-031-73748-0_3},
  doi		= {10.1007/978-3-031-73748-0_3},
  booktitle	= {Data Engineering in Medical Imaging},
  publisher	= {Springer Nature Switzerland},
  author	= {Roschewitz, Mélanie and de Sousa Ribeiro, Fabio and Xia,
		  Tian and Khara, Galvin and Glocker, Ben},
  year		= {2024},
  month		= oct,
  pages		= {22–32}
}

@InProceedings{	  sae_survrnc_miccai2024,
  author	= { Saeed, Numan and Ridzuan, Muhammad and Maani, Fadillah
		  Adamsyah and Alasmawi, Hussain and Nandakumar, Karthik and
		  Yaqub, Mohammad},
  title		= { { SurvRNC: Learning Ordered Representations for Survival
		  Prediction using Rank-N-Contrast } },
  booktitle	= {proceedings of Medical Image Computing and Computer
		  Assisted Intervention -- MICCAI 2024},
  year		= {2024},
  publisher	= {Springer Nature Switzerland},
  volume	= {LNCS 15005},
  month		= {October},
  page		= {659 -- 669}
}

@Article{	  salaffi:2019:radiographic_scoring_methods,
  title		= {Radiographic scoring methods in rheumatoid arthritis and
		  psoriatic arthritis},
  volume	= {124},
  issn		= {1826-6983},
  url		= {http://dx.doi.org/10.1007/s11547-019-01001-3},
  doi		= {10.1007/s11547-019-01001-3},
  number	= {11},
  journal	= {La radiologia medica},
  publisher	= {Springer Science and Business Media LLC},
  author	= {Salaffi, Fausto and Carotti, Marina and Beci, Giacomo and
		  Di Carlo, Marco and Giovagnoni, Andrea},
  year		= {2019},
  month		= feb,
  pages		= {1071–1086}
}

@Article{	  schlereth_deep_2024,
  title		= {Deep learning-based classification of erosion, synovitis
		  and osteitis in hand {MRI} of patients with inflammatory
		  arthritis},
  volume	= {10},
  issn		= {2056-5933},
  doi		= {10.1136/rmdopen-2024-004273},
  abstract	= {OBJECTIVES: To train, test and validate the performance of
		  a convolutional neural network (CNN)-based approach for the
		  automated assessment of bone erosions, osteitis and
		  synovitis in hand MRI of patients with inflammatory
		  arthritis. METHODS: Hand MRIs (coronal T1-weighted,
		  T2-weighted fat-suppressed, T1-weighted fat-suppressed
		  contrast-enhanced) of rheumatoid arthritis (RA) and
		  psoriatic arthritis (PsA) patients from the rheumatology
		  department of the Erlangen University Hospital were
		  assessed by two expert rheumatologists using the Outcome
		  Measures in Rheumatology-validated RA MRI Scoring System
		  and PsA MRI Scoring System scores and were used to train,
		  validate and test CNNs to automatically score erosions,
		  osteitis and synovitis. Scoring performance was compared
		  with human annotations in terms of macro-area under the
		  receiver operating characteristic curve (AUC) and balanced
		  accuracy using fivefold cross-validation. Validation was
		  performed on an independent dataset of MRIs from a second
		  patient cohort. RESULTS: In total, 211 MRIs from 112
		  patients (14 906 region of interests (ROIs)) were included
		  for training/internal validation using cross-validation and
		  220 MRIs from 75 patients (11 040 ROIs) for external
		  validation of the networks. The networks achieved high mean
		  (SD) macro-AUC of 92\%±1\% for erosions, 91\%±2\% for
		  osteitis and 85\%±2\% for synovitis. Compared with human
		  annotation, CNNs achieved a high mean Spearman correlation
		  for erosions (90±2\%), osteitis (78±8\%) and synovitis
		  (69±7\%), which remained consistent in the validation
		  dataset. CONCLUSIONS: We developed a CNN-based automated
		  scoring system that allowed a rapid grading of erosions,
		  osteitis and synovitis with good diagnostic accuracy and
		  using less MRI sequences compared with conventional
		  scoring. This CNN-based approach may help develop
		  standardised cost-efficient and time-efficient assessments
		  of hand MRIs for patients with arthritis.},
  language	= {eng},
  number	= {2},
  journal	= {RMD open},
  author	= {Schlereth, Maja and Mutlu, Melek Yalcin and Utz, Jonas and
		  Bayat, Sara and Heimann, Tobias and Qiu, Jingna and Ehring,
		  Chris and Liu, Chang and Uder, Michael and Kleyer, Arnd and
		  Simon, David and Roemer, Frank and Schett, Georg and
		  Breininger, Katharina and Fagni, Filippo},
  month		= jun,
  year		= {2024},
  pmid		= {38886001},
  pmcid		= {PMC11184189},
  keywords	= {Adult, Aged, Arthritis, Rheumatoid, Deep Learning, Female,
		  Humans, Male, Middle Aged, rheumatoid arthritis, Severity
		  of Illness Index, Arthritis, Psoriatic, classification,
		  Hand, machine learning, magnetic resonance imaging,
		  Magnetic Resonance Imaging, Neural Networks, Computer,
		  Osteitis, psoriatic arthritis, ROC Curve, Synovitis},
  pages		= {e004273}
}

@Misc{		  schölkopf2021causalrepresentationlearning,
  title		= {Towards Causal Representation Learning},
  author	= {Bernhard Schölkopf and Francesco Locatello and Stefan
		  Bauer and Nan Rosemary Ke and Nal Kalchbrenner and Anirudh
		  Goyal and Yoshua Bengio},
  year		= {2021},
  eprint	= {2102.11107},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2102.11107}
}

@Article{	  sequi-sabater_artificial_2025,
  title		= {Artificial intelligence in rheumatology research: what is
		  it good for?},
  volume	= {11},
  copyright	= {This is an open access article distributed in accordance
		  with the Creative Commons Attribution Non Commercial (CC
		  BY-NC 4.0) license},
  issn		= {2056-5933},
  shorttitle	= {Artificial intelligence in rheumatology research},
  url		= {https://rmdopen.bmj.com/content/11/1/e004309},
  doi		= {10.1136/rmdopen-2024-004309},
  abstract	= {Artificial intelligence (AI) is transforming rheumatology
		  research, with a myriad of studies aiming to improve
		  diagnosis, prognosis and treatment prediction, while also
		  showing potential capability to optimise the research
		  workflow, improve drug discovery and clinical trials.
		  Machine learning, a key element of discriminative AI, has
		  demonstrated the ability of accurately classifying
		  rheumatic diseases and predicting therapeutic outcomes by
		  using diverse data types, including structured databases,
		  imaging and text. In parallel, generative AI, driven by
		  large language models, is becoming a powerful tool for
		  optimising the research workflow by supporting with content
		  generation, literature review automation and clinical
		  decision support. This review explores the current
		  applications and future potential of both discriminative
		  and generative AI in rheumatology. It also highlights the
		  challenges posed by these technologies, such as ethical
		  concerns and the need for rigorous validation and
		  regulatory oversight. The integration of AI in rheumatology
		  promises substantial advancements but requires a balanced
		  approach to optimise benefits and minimise potential
		  possible downsides.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-02},
  journal	= {RMD Open},
  author	= {Sequí-Sabater, José Miguel and Benavent, Diego},
  month		= jan,
  year		= {2025},
  pmid		= {10.1136/rmdopen-2024-004309},
  note		= {Publisher: EULAR},
  file		= {Full Text
		  PDF:/home/cwatzenboeck/Zotero/storage/F6FHJLYS/Sequí-Sabater
		  und Benavent - 2025 - Artificial intelligence in
		  rheumatology research what is it good
		  for.pdf:application/pdf}
}

@Article{	  shrout1979intraclasscu,
  author	= {Shrout, Patrick E. and Fleiss, Joseph L.},
  journal	= {Psychological Bulletin},
  title		= {Intraclass correlations: Uses in assessing rater
		  reliability},
  year		= {1979},
  volume	= {86},
  number	= {2},
  pages		= {420-428},
  doi		= {10.1037/0033-2909.86.2.420}
}

@Misc{		  simeoni_dinov3_2025,
  title		= {{DINOv3}},
  url		= {http://arxiv.org/abs/2508.10104},
  doi		= {10.48550/arXiv.2508.10104},
  abstract	= {Self-supervised learning holds the promise of eliminating
		  the need for manual data annotation, enabling models to
		  scale effortlessly to massive datasets and larger
		  architectures. By not being tailored to specific tasks or
		  domains, this training paradigm has the potential to learn
		  visual representations from diverse sources, ranging from
		  natural to aerial images -- using a single algorithm. This
		  technical report introduces DINOv3, a major milestone
		  toward realizing this vision by leveraging simple yet
		  effective strategies. First, we leverage the benefit of
		  scaling both dataset and model size by careful data
		  preparation, design, and optimization. Second, we introduce
		  a new method called Gram anchoring, which effectively
		  addresses the known yet unsolved issue of dense feature
		  maps degrading during long training schedules. Finally, we
		  apply post-hoc strategies that further enhance our models'
		  flexibility with respect to resolution, model size, and
		  alignment with text. As a result, we present a versatile
		  vision foundation model that outperforms the specialized
		  state of the art across a broad range of settings, without
		  fine-tuning. DINOv3 produces high-quality dense features
		  that achieve outstanding performance on various vision
		  tasks, significantly surpassing previous self- and
		  weakly-supervised foundation models. We also share the
		  DINOv3 suite of vision models, designed to advance the
		  state of the art on a wide spectrum of tasks and data by
		  providing scalable solutions for diverse resource
		  constraints and deployment scenarios.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Siméoni, Oriane and Vo, Huy V. and Seitzer, Maximilian
		  and Baldassarre, Federico and Oquab, Maxime and Jose, Cijo
		  and Khalidov, Vasil and Szafraniec, Marc and Yi, Seungeun
		  and Ramamonjisoa, Michaël and Massa, Francisco and Haziza,
		  Daniel and Wehrstedt, Luca and Wang, Jianyuan and Darcet,
		  Timothée and Moutakanni, Théo and Sentana, Leonel and
		  Roberts, Claire and Vedaldi, Andrea and Tolan, Jamie and
		  Brandt, John and Couprie, Camille and Mairal, Julien and
		  Jégou, Hervé and Labatut, Patrick and Bojanowski, Piotr},
  month		= aug,
  year		= {2025},
  note		= {arXiv:2508.10104 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning},
  file		= {Snapshot:/home/cwatzenboeck/Zotero/storage/2BZ58XQR/2508.html:text/html}
}

@Article{	  sun_artificial_2025,
  title		= {Artificial intelligence in rheumatoid arthritis},
  volume	= {05},
  url		= {https://mednexus.org/doi/10.1002/rai2.12171},
  doi		= {10.1002/rai2.12171},
  abstract	= {Rheumatoid arthritis (RA) is a chronic autoimmune
		  condition that causes joint inflammation and damage and
		  significantly affects patients' quality of life. Over the
		  past 5 years, the application of artificial intelligence
		  (AI), particularly deep learning, has resulted in notable
		  advancements in the field of rheumatology. This review
		  explores these developments, highlighting how AI has
		  enhanced the precision and reliability of imaging
		  techniques, such as radiography, ultrasound imaging, and
		  magnetic resonance imaging, for managing RA. In addition,
		  the integration of diverse data sources, including clinical
		  records, genetic profiles, and imaging examinations, has
		  facilitated more accurate predictions and formulation of
		  personalized treatment strategies. However, challenges such
		  as data variability, complexity of AI models, and ethical
		  considerations remain. Addressing these issues is essential
		  for further progress. Future research should focus on
		  improving data integration, model interpretability, and
		  ethical deployment of AI in clinical practice. These
		  advancements have the potential to significantly improve
		  the diagnosis and management of RA, moving closer to the
		  goals of precision medicine in this field.},
  number	= {02},
  urldate	= {2025-10-07},
  journal	= {Rheumatology \& Autoimmunity},
  author	= {Sun, Yiduo and Lin, Jin and Chen, Weiqian},
  month		= jun,
  year		= {2025},
  note		= {Publisher: Wiley},
  pages		= {88--100}
}

@Article{	  sun_crowdsourcing_2022,
  title		= {A {Crowdsourcing} {Approach} to {Develop} {Machine}
		  {Learning} {Models} to {Quantify} {Radiographic} {Joint}
		  {Damage} in {Rheumatoid} {Arthritis}},
  volume	= {5},
  issn		= {2574-3805},
  doi		= {10.1001/jamanetworkopen.2022.27423},
  abstract	= {IMPORTANCE: An automated, accurate method is needed for
		  unbiased assessment quantifying accrual of joint space
		  narrowing and erosions on radiographic images of the hands
		  and wrists, and feet for clinical trials, monitoring of
		  joint damage over time, assisting rheumatologists with
		  treatment decisions. Such a method has the potential to be
		  directly integrated into electronic health records.
		  OBJECTIVES: To design and implement an international
		  crowdsourcing competition to catalyze the development of
		  machine learning methods to quantify radiographic damage in
		  rheumatoid arthritis (RA). DESIGN, SETTING, AND
		  PARTICIPANTS: This diagnostic/prognostic study describes
		  the Rheumatoid Arthritis 2-Dialogue for Reverse Engineering
		  Assessment and Methods (RA2-DREAM Challenge), which used
		  existing radiographic images and expert-curated Sharp-van
		  der Heijde (SvH) scores from 2 clinical studies (674
		  radiographic sets from 562 patients) for training (367
		  sets), leaderboard (119 sets), and final evaluation (188
		  sets). Challenge participants were tasked with developing
		  methods to automatically quantify overall damage
		  (subchallenge 1), joint space narrowing (subchallenge 2),
		  and erosions (subchallenge 3). The challenge was finished
		  on June 30, 2020. MAIN OUTCOMES AND MEASURES: Scores
		  derived from submitted algorithms were compared with the
		  expert-curated SvH scores, and a baseline model was created
		  for benchmark comparison. Performances were ranked using
		  weighted root mean square error (RMSE). The performance and
		  reproductivity of each algorithm was assessed using Bayes
		  factor from bootstrapped data, and further evaluated with a
		  postchallenge independent validation data set. RESULTS: The
		  RA2-DREAM Challenge received a total of 173 submissions
		  from 26 participants or teams in 7 countries for the
		  leaderboard round, and 13 submissions were included in the
		  final evaluation. The weighted RMSEs metric showed that the
		  winning algorithms produced scores that were very close to
		  the expert-curated SvH scores. Top teams included Team
		  Shirin for subchallenge 1 (weighted RMSE, 0.44), HYL-YFG
		  (Hongyang Li and Yuanfang Guan) subchallenge 2 (weighted
		  RMSE, 0.38), and Gold Therapy for subchallenge 3 (weighted
		  RMSE, 0.43). Bootstrapping/Bayes factor approach and the
		  postchallenge independent validation confirmed the
		  reproducibility and the estimation concordance indices
		  between final evaluation and postchallenge independent
		  validation data set were 0.71 for subchallenge 1, 0.78 for
		  subchallenge 2, and 0.82 for subchallenge 3. CONCLUSIONS
		  AND RELEVANCE: The RA2-DREAM Challenge resulted in the
		  development of algorithms that provide feasible, quick, and
		  accurate methods to quantify joint damage in RA.
		  Ultimately, these methods could help research studies on RA
		  joint damage and may be integrated into electronic health
		  records to help clinicians serve patients better by
		  providing timely, reliable, and quantitative information
		  for making treatment decisions to prevent further damage.},
  language	= {eng},
  number	= {8},
  journal	= {JAMA network open},
  author	= {Sun, Dongmei and Nguyen, Thanh M. and Allaway, Robert J.
		  and Wang, Jelai and Chung, Verena and Yu, Thomas V. and
		  Mason, Michael and Dimitrovsky, Isaac and Ericson, Lars and
		  Li, Hongyang and Guan, Yuanfang and Israel, Ariel and Olar,
		  Alex and Pataki, Balint Armin and Stolovitzky, Gustavo and
		  Guinney, Justin and Gulko, Percio S. and Frazier, Mason B.
		  and Chen, Jake Y. and Costello, James C. and Bridges, S.
		  Louis and {RA2-DREAM Challenge Community}},
  month		= aug,
  year		= {2022},
  pmid		= {36036935},
  pmcid		= {PMC9425151},
  keywords	= {Arthritis, Rheumatoid, Humans, Bayes Theorem,
		  Crowdsourcing, Machine Learning, Reproducibility of
		  Results},
  pages		= {e2227423}
}

@Misc{		  szegedy2015rethinkinginceptionarchitecturecomputer,
  title		= {Rethinking the Inception Architecture for Computer
		  Vision},
  author	= {Christian Szegedy and Vincent Vanhoucke and Sergey Ioffe
		  and Jonathon Shlens and Zbigniew Wojna},
  year		= {2015},
  eprint	= {1512.00567},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.CV},
  url		= {https://arxiv.org/abs/1512.00567}
}

@Misc{		  tan_rheumatoid_2021,
  title		= {Rheumatoid {Arthritis}: {Automated} {Scoring} of
		  {Radiographic} {Joint} {Damage}},
  shorttitle	= {Rheumatoid {Arthritis}},
  url		= {http://arxiv.org/abs/2110.08812},
  doi		= {10.48550/arXiv.2110.08812},
  abstract	= {Rheumatoid arthritis is an autoimmune disease that causes
		  joint damage due to inflammation in the soft tissue lining
		  the joints known as the synovium. It is vital to identify
		  joint damage as soon as possible to provide necessary
		  treatment early and prevent further damage to the bone
		  structures. Radiographs are often used to assess the extent
		  of the joint damage. Currently, the scoring of joint damage
		  from the radiograph takes expertise, effort, and time.
		  Joint damage associated with rheumatoid arthritis is also
		  not quantitated in clinical practice and subjective
		  descriptors are used. In this work, we describe a pipeline
		  of deep learning models to automatically identify and score
		  rheumatoid arthritic joint damage from a radiographic
		  image. Our automatic tool was shown to produce scores with
		  extremely high balanced accuracy within a couple of minutes
		  and utilizing this would remove the subjectivity of the
		  scores between human reviewers.},
  urldate	= {2025-10-09},
  publisher	= {arXiv},
  author	= {Tan, Yan Ming and Chong, Raphael Quek Hao and Hargreaves,
		  Carol Anne},
  month		= oct,
  year		= {2021},
  note		= {arXiv:2110.08812 [eess]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning,
		  Electrical Engineering and Systems Science - Image and
		  Video Processing}
}

@Misc{		  terminassian2024explainableaisurvivalanalysis,
  title		= {Explainable AI for survival analysis: a median-SHAP
		  approach},
  author	= {Lucile Ter-Minassian and Sahra Ghalebikesabi and Karla
		  Diaz-Ordaz and Chris Holmes},
  year		= {2024},
  eprint	= {2402.00072},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2402.00072}
}

@Article{	  tiu_expert-level_2022,
  title		= {Expert-level detection of pathologies from unannotated
		  chest {X}-ray images via self-supervised learning},
  volume	= {6},
  copyright	= {2022 The Author(s)},
  issn		= {2157-846X},
  url		= {https://www.nature.com/articles/s41551-022-00936-9},
  doi		= {10.1038/s41551-022-00936-9},
  abstract	= {In tasks involving the interpretation of medical images,
		  suitably trained machine-learning models often exceed the
		  performance of medical experts. Yet such a high-level of
		  performance typically requires that the models be trained
		  with relevant datasets that have been painstakingly
		  annotated by experts. Here we show that a self-supervised
		  model trained on chest X-ray images that lack explicit
		  annotations performs pathology-classification tasks with
		  accuracies comparable to those of radiologists. On an
		  external validation dataset of chest X-rays, the
		  self-supervised model outperformed a fully supervised model
		  in the detection of three pathologies (out of eight), and
		  the performance generalized to pathologies that were not
		  explicitly annotated for model training, to multiple
		  image-interpretation tasks and to datasets from multiple
		  institutions.},
  language	= {en},
  number	= {12},
  urldate	= {2025-10-07},
  journal	= {Nature Biomedical Engineering},
  author	= {Tiu, Ekin and Talius, Ellie and Patel, Pujan and Langlotz,
		  Curtis P. and Ng, Andrew Y. and Rajpurkar, Pranav},
  month		= dec,
  year		= {2022},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Health care, Medical imaging},
  pages		= {1399--1406}
}

@Article{	  vanderheijde2000,
  author	= {D. van der Heijde},
  title		= {How to Read Radiographs According to the Sharp/van der
		  Heijde Method},
  journal	= {The Journal of Rheumatology},
  year		= {2000},
  volume	= {27},
  number	= {1},
  pages		= {261--263},
  note		= {Corrected and republished},
  doi		= {},
  url		= {},
  pmid		= {10648051}
}

@Misc{		  veldhuizen_foundation_2025,
  title		= {Foundation {Models} in {Medical} {Imaging} -- {A} {Review}
		  and {Outlook}},
  url		= {http://arxiv.org/abs/2506.09095},
  doi		= {10.48550/arXiv.2506.09095},
  abstract	= {Foundation models (FMs) are changing the way medical
		  images are analyzed by learning from large collections of
		  unlabeled data. Instead of relying on manually annotated
		  examples, FMs are pre-trained to learn general-purpose
		  visual features that can later be adapted to specific
		  clinical tasks with little additional supervision. In this
		  review, we examine how FMs are being developed and applied
		  in pathology, radiology, and ophthalmology, drawing on
		  evidence from over 150 studies. We explain the core
		  components of FM pipelines, including model architectures,
		  self-supervised learning methods, and strategies for
		  downstream adaptation. We also review how FMs are being
		  used in each imaging domain and compare design choices
		  across applications. Finally, we discuss key challenges and
		  open questions to guide future research.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Veldhuizen, Vivien van and Botha, Vanessa and Lu, Chunyao
		  and Cesur, Melis Erdal and Lipman, Kevin Groot and Jong,
		  Edwin D. de and Horlings, Hugo and Sanchez, Clárisa I. and
		  Snoek, Cees G. M. and Wessels, Lodewyk and Mann, Ritse and
		  Marcus, Eric and Teuwen, Jonas},
  month		= jun,
  year		= {2025},
  note		= {arXiv:2506.09095 [eess]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Artificial Intelligence,
		  Electrical Engineering and Systems Science - Image and
		  Video Processing}
}

@Article{	  venalainen_deep_2025,
  title		= {Deep learning enables automatic detection of joint damage
		  progression in rheumatoid arthritis-model development and
		  external validation},
  volume	= {64},
  issn		= {1462-0332},
  doi		= {10.1093/rheumatology/keae215},
  abstract	= {OBJECTIVES: Although deep learning has demonstrated
		  substantial potential in automatic quantification of joint
		  damage in RA, evidence for detecting longitudinal changes
		  at an individual patient level is lacking. Here, we
		  introduce and externally validate our automated RA scoring
		  algorithm (AuRA), and demonstrate its utility for
		  monitoring radiographic progression in a real-world
		  setting. METHODS: The algorithm, originally developed
		  during the Rheumatoid Arthritis 2-Dialogue for Reverse
		  Engineering Assessment and Methods (RA2-DREAM) challenge,
		  was trained to predict expert-curated Sharp-van der Heijde
		  total scores in hand and foot radiographs from two previous
		  clinical studies (n = 367). We externally validated
		  AuRA against data (n = 205) from Turku University
		  Hospital and compared the performance against two
		  top-performing RA2-DREAM solutions. Finally, for 54
		  patients, we extracted additional radiograph sets from
		  another control visit to the clinic (average time interval
		  of 4.6 years). RESULTS: In the external validation
		  cohort, with a root mean square error (RMSE) of 23.6, AuRA
		  outperformed both top-performing RA2-DREAM algorithms
		  (RMSEs 35.0 and 35.6). The improved performance was
		  explained mostly by lower errors at higher expert-assessed
		  scores. The longitudinal changes predicted by our algorithm
		  were significantly correlated with changes in
		  expert-assessed scores (Pearson's R = 0.74,
		  P {\textless} 0.001). CONCLUSION: AuRA had the best
		  external validation performance and demonstrated potential
		  for detecting longitudinal changes in joint damage.
		  Available from https://hub.docker.com/r/elolab/aura, our
		  algorithm can easily be applied for automatic detection of
		  radiographic progression in the future, reducing the need
		  for laborious manual scoring.},
  language	= {eng},
  number	= {3},
  journal	= {Rheumatology (Oxford, England)},
  author	= {Venäläinen, Mikko S. and Biehl, Alexander and Holstila,
		  Milja and Kuusalo, Laura and Elo, Laura L.},
  month		= mar,
  year		= {2025},
  pmid		= {38597875},
  pmcid		= {PMC11879318},
  keywords	= {deep learning, Adult, Aged, Algorithms, Arthritis,
		  Rheumatoid, Deep Learning, disease progression, Disease
		  Progression, Female, Foot Joints, Hand Joints, Humans,
		  Male, Middle Aged, radiographic joint damage, radiographic
		  scoring, Radiography, rheumatoid arthritis, Severity of
		  Illness Index},
  pages		= {1068--1076}
}

@Article{	  venlinen2024,
  title		= {Deep learning enables automatic detection of joint damage
		  progression in rheumatoid arthritis—model development and
		  external validation},
  volume	= {64},
  issn		= {1462-0332},
  url		= {http://dx.doi.org/10.1093/rheumatology/keae215},
  doi		= {10.1093/rheumatology/keae215},
  number	= {3},
  journal	= {Rheumatology},
  publisher	= {Oxford University Press (OUP)},
  author	= {Ven\"{a}l\"{a}inen, Mikko S and Biehl, Alexander and
		  Holstila, Milja and Kuusalo, Laura and Elo, Laura L},
  year		= {2024},
  month		= apr,
  pages		= {1068–1076}
}

@Article{	  wang_editorial_2025,
  title		= {Editorial for {Special} {Issue} on {Foundation} {Models}
		  for {Medical} {Image} {Analysis}},
  volume	= {100},
  issn		= {13618415},
  url		= {https://linkinghub.elsevier.com/retrieve/pii/S1361841524003141},
  doi		= {10.1016/j.media.2024.103389},
  language	= {en},
  urldate	= {2025-10-07},
  journal	= {Medical Image Analysis},
  author	= {Wang, Xiaosong and Wang, Dequan and Li, Xiaoxiao and
		  Rittscher, Jens and Metaxas, Dimitris and Zhang, Shaoting},
  month		= feb,
  year		= {2025},
  pages		= {103389}
}

@Article{	  wasserthal2023,
  author	= {Wasserthal, Jakob and Breit, Hanns-Christian and Meyer,
		  Manfred T. and Pradella, Maurice and Hinck, Daniel and
		  Sauter, Alexander W. and Heye, Tobias and Boll, Daniel T.
		  and Cyriac, Joshy and Yang, Shan and Bach, Michael and
		  Segeroth, Martin},
  title		= {TotalSegmentator: Robust Segmentation of 104 Anatomic
		  Structures in CT Images},
  journal	= {Radiology: Artificial Intelligence},
  volume	= {5},
  number	= {5},
  pages		= {e230024},
  year		= {2023},
  doi		= {10.1148/ryai.230024},
  url		= {https://doi.org/10.1148/ryai.230024},
  eprint	= {https://doi.org/10.1148/ryai.230024},
  abstract	= { Purpose To present a deep learning segmentation model
		  that can automatically and robustly segment all major
		  anatomic structures on body CT images. Materials and
		  Methods In this retrospective study, 1204 CT examinations
		  (from 2012, 2016, and 2020) were used to segment 104
		  anatomic structures (27 organs, 59 bones, 10 muscles, and
		  eight vessels) relevant for use cases such as organ
		  volumetry, disease characterization, and surgical or
		  radiation therapy planning. The CT images were randomly
		  sampled from routine clinical studies and thus represent a
		  real-world dataset (different ages, abnormalities,
		  scanners, body parts, sequences, and sites). The authors
		  trained an nnU-Net segmentation algorithm on this dataset
		  and calculated Dice similarity coefficients to evaluate the
		  model’s performance. The trained algorithm was applied to
		  a second dataset of 4004 whole-body CT examinations to
		  investigate age-dependent volume and attenuation changes.
		  Results The proposed model showed a high Dice score (0.943)
		  on the test set, which included a wide range of clinical
		  data with major abnormalities. The model significantly
		  outperformed another publicly available segmentation model
		  on a separate dataset (Dice score, 0.932 vs 0.871; P <
		  .001). The aging study demonstrated significant
		  correlations between age and volume and mean attenuation
		  for a variety of organ groups (eg, age and aortic volume
		  [rs = 0.64; P < .001]; age and mean attenuation of the
		  autochthonous dorsal musculature [rs = −0.74; P < .001]).
		  Conclusion The developed model enables robust and accurate
		  segmentation of 104 anatomic structures. The annotated
		  dataset (https://doi.org/10.5281/zenodo.6802613) and
		  toolkit (https://www.github.com/wasserth/TotalSegmentator)
		  are publicly available. Keywords: CT, Segmentation, Neural
		  Networks Supplemental material is available for this
		  article. © RSNA, 2023 See also commentary by Sebro and
		  Mongan in this issue. }
}

@InProceedings{	  weng:ieee2025:enhancing_multimodal_sentiment_analysis:via_rnc,
  author	= {Weng, Yuzhe and Wang, Haotian and Gao, Tian and Li, Kewei
		  and Niu, Shutong and Du, Jun},
  booktitle	= {ICASSP 2025 - 2025 IEEE International Conference on
		  Acoustics, Speech and Signal Processing (ICASSP)},
  title		= {Enhancing Multimodal Sentiment Analysis for Missing
		  Modality through Self-Distillation and Unified Modality
		  Cross-Attention},
  year		= {2025},
  volume	= {},
  number	= {},
  pages		= {1-5},
  keywords	= {Sentiment
		  analysis;Costs;Codes;Annotations;Autoencoders;Signal
		  processing;Robustness;Speech processing;Testing;Automatic
		  speech recognition;Multimodal Fusion;Sentiment
		  Analysis;Self Distillation;Missing Modality},
  doi		= {10.1109/ICASSP49660.2025.10889029}
}

@Misc{		  wu_deep_2024,
  title		= {Deep {Multimodal} {Learning} with {Missing} {Modality}:
		  {A} {Survey}},
  shorttitle	= {Deep {Multimodal} {Learning} with {Missing} {Modality}},
  url		= {http://arxiv.org/abs/2409.07825},
  doi		= {10.48550/arXiv.2409.07825},
  abstract	= {During multimodal model training and testing, certain data
		  modalities may be absent due to sensor limitations, cost
		  constraints, privacy concerns, or data loss, negatively
		  affecting performance. Multimodal learning techniques
		  designed to handle missing modalities can mitigate this by
		  ensuring model robustness even when some modalities are
		  unavailable. This survey reviews recent progress in
		  Multimodal Learning with Missing Modality (MLMM), focusing
		  on deep learning methods. It provides the first
		  comprehensive survey that covers the motivation and
		  distinctions between MLMM and standard multimodal learning
		  setups, followed by a detailed analysis of current methods,
		  applications, and datasets, concluding with challenges and
		  future directions.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Wu, Renjie and Wang, Hu and Chen, Hsiang-Ting and
		  Carneiro, Gustavo},
  month		= oct,
  year		= {2024},
  note		= {arXiv:2409.07825 [cs] version: 3},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning, Computer
		  Science - Artificial Intelligence}
}

@Article{	  wu_vision-language_2025,
  title		= {Vision-language foundation model for {3D} medical
		  imaging},
  volume	= {1},
  copyright	= {2025 The Author(s)},
  issn		= {3005-1460},
  url		= {https://www.nature.com/articles/s44387-025-00015-9},
  doi		= {10.1038/s44387-025-00015-9},
  abstract	= {Recent advances in AI, especially vision-language
		  foundation models (VLFMs), show promise in automating
		  radiology report generation from complex 3D medical imaging
		  data. Our review analyzes 23 studies on VLFMs, focusing on
		  model architectures, capabilities, training datasets, and
		  evaluation metrics. We discuss AI’s evolution in
		  radiology, emphasizing the need for diverse datasets and
		  standardized metrics, as challenges remain in producing
		  consistent, high-quality reports.},
  language	= {en},
  number	= {1},
  urldate	= {2025-10-07},
  journal	= {npj Artificial Intelligence},
  author	= {Wu, Jing and Wang, Yuli and Zhong, Zhusi and Liao, Weihua
		  and Trayanova, Natalia and Jiao, Zhicheng and Bai, Harrison
		  X.},
  month		= aug,
  year		= {2025},
  note		= {Publisher: Nature Publishing Group},
  keywords	= {Computer science, Computational models},
  pages		= {17}
}

@InProceedings{	  yang2021delving,
  title		= {Delving into deep imbalanced regression},
  author	= {Yang, Yuzhe and Zha, Kaiwen and Chen, Yingcong and Wang,
		  Hao and Katabi, Dina},
  booktitle	= {International conference on machine learning},
  pages		= {11842--11851},
  year		= {2021},
  organization	= {PMLR}
}

@Misc{		  yang2021delvingdeepimbalancedregression,
  title		= {Delving into Deep Imbalanced Regression},
  author	= {Yuzhe Yang and Kaiwen Zha and Ying-Cong Chen and Hao Wang
		  and Dina Katabi},
  year		= {2021},
  eprint	= {2102.09554},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2102.09554}
}

@InProceedings{	  zeghlache:miccai2024:latim,
  author	= { Zeghlache, Rachid and Conze, Pierre-Henri and El Habib
		  Daho, Mostafa and Li, Yihao and Le Boité, Hugo and
		  Tadayoni, Ramin and Massin, Pascale and Cochener, Béatrice
		  and Rezaei, Alireza and Brahim, Ikram and Quellec, Gwenolé
		  and Lamard, Mathieu},
  title		= { { LaTiM: Longitudinal representation learning in
		  continuous-time models to predict disease progression } },
  booktitle	= {proceedings of Medical Image Computing and Computer
		  Assisted Intervention -- MICCAI 2024},
  year		= {2024},
  publisher	= {Springer Nature Switzerland},
  volume	= {LNCS 15005},
  month		= {October},
  page		= {404 -- 414}
}

@Misc{		  zha2023rankncontrastlearningcontinuousrepresentations,
  title		= {Rank-N-Contrast: Learning Continuous Representations for
		  Regression},
  author	= {Kaiwen Zha and Peng Cao and Jeany Son and Yuzhe Yang and
		  Dina Katabi},
  year		= {2023},
  eprint	= {2210.01189},
  archiveprefix	= {arXiv},
  primaryclass	= {cs.LG},
  url		= {https://arxiv.org/abs/2210.01189}
}

@Misc{		  zha_rank-n-contrast_2023,
  title		= {Rank-{N}-{Contrast}: {Learning} {Continuous}
		  {Representations} for {Regression}},
  shorttitle	= {Rank-{N}-{Contrast}},
  url		= {http://arxiv.org/abs/2210.01189},
  doi		= {10.48550/arXiv.2210.01189},
  abstract	= {Deep regression models typically learn in an end-to-end
		  fashion without explicitly emphasizing a regression-aware
		  representation. Consequently, the learned representations
		  exhibit fragmentation and fail to capture the continuous
		  nature of sample orders, inducing suboptimal results across
		  a wide range of regression tasks. To fill the gap, we
		  propose Rank-N-Contrast (RNC), a framework that learns
		  continuous representations for regression by contrasting
		  samples against each other based on their rankings in the
		  target space. We demonstrate, theoretically and
		  empirically, that RNC guarantees the desired order of
		  learned representations in accordance with the target
		  orders, enjoying not only better performance but also
		  significantly improved robustness, efficiency, and
		  generalization. Extensive experiments using five real-world
		  regression datasets that span computer vision,
		  human-computer interaction, and healthcare verify that RNC
		  achieves state-of-the-art performance, highlighting its
		  intriguing properties including better data efficiency,
		  robustness to spurious targets and data corruptions, and
		  generalization to distribution shifts. Code is available
		  at: https://github.com/kaiwenzha/Rank-N-Contrast.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Zha, Kaiwen and Cao, Peng and Son, Jeany and Yang, Yuzhe
		  and Katabi, Dina},
  month		= oct,
  year		= {2023},
  note		= {arXiv:2210.01189 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Machine Learning, Computer
		  Science - Artificial Intelligence}
}

@Misc{		  zhang_biomedclip_2025,
  title		= {{BiomedCLIP}: a multimodal biomedical foundation model
		  pretrained from fifteen million scientific image-text
		  pairs},
  shorttitle	= {{BiomedCLIP}},
  url		= {http://arxiv.org/abs/2303.00915},
  doi		= {10.48550/arXiv.2303.00915},
  abstract	= {Biomedical data is inherently multimodal, comprising
		  physical measurements and natural language narratives. A
		  generalist biomedical AI model needs to simultaneously
		  process different modalities of data, including text and
		  images. Therefore, training an effective generalist
		  biomedical model requires high-quality multimodal data,
		  such as parallel image-text pairs. Here, we present
		  PMC-15M, a novel dataset that is two orders of magnitude
		  larger than existing biomedical multimodal datasets such as
		  MIMIC-CXR, and spans a diverse range of biomedical image
		  types. PMC-15M contains 15 million biomedical image-text
		  pairs collected from 4.4 million scientific articles. Based
		  on PMC-15M, we have pretrained BiomedCLIP, a multimodal
		  foundation model, with domain-specific adaptations tailored
		  to biomedical vision-language processing. We conducted
		  extensive experiments and ablation studies on standard
		  biomedical imaging tasks from retrieval to classification
		  to visual question-answering (VQA). BiomedCLIP achieved new
		  state-of-the-art results in a wide range of standard
		  datasets, substantially outperforming prior approaches.
		  Intriguingly, by large-scale pretraining on diverse
		  biomedical image types, BiomedCLIP even outperforms
		  state-of-the-art radiology-specific models such as BioViL
		  in radiology-specific tasks such as RSNA pneumonia
		  detection. In summary, BiomedCLIP is a fully open-access
		  foundation model that achieves state-of-the-art performance
		  on various biomedical tasks, paving the way for
		  transformative multimodal biomedical discovery and
		  applications. We release our models at
		  https://aka.ms/biomedclip to facilitate future research in
		  multimodal biomedical AI.},
  urldate	= {2025-11-10},
  publisher	= {arXiv},
  author	= {Zhang, Sheng and Xu, Yanbo and Usuyama, Naoto and Xu,
		  Hanwen and Bagga, Jaspreet and Tinn, Robert and Preston,
		  Sam and Rao, Rajesh and Wei, Mu and Valluri, Naveen and
		  Wong, Cliff and Tupini, Andrea and Wang, Yu and Mazzola,
		  Matt and Shukla, Swadheen and Liden, Lars and Gao, Jianfeng
		  and Crabtree, Angela and Piening, Brian and Bifulco, Carlo
		  and Lungren, Matthew P. and Naumann, Tristan and Wang,
		  Sheng and Poon, Hoifung},
  month		= jan,
  year		= {2025},
  note		= {arXiv:2303.00915 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition, Computer Science - Computation and Language}
}

@Article{	  zhou_rating_2022,
  title		= {{RATING}: {Medical} knowledge-guided rheumatoid arthritis
		  assessment from multimodal ultrasound images via deep
		  learning},
  volume	= {3},
  issn		= {2666-3899},
  shorttitle	= {{RATING}},
  url		= {https://www.sciencedirect.com/science/article/pii/S2666389922002100},
  doi		= {10.1016/j.patter.2022.100592},
  abstract	= {Multimodal ultrasound has demonstrated its power in the
		  clinical assessment of rheumatoid arthritis (RA). However,
		  for radiologists, it requires strong experience. In this
		  paper, we propose a rheumatoid arthritis knowledge guided
		  (RATING) system that automatically scores the RA activity
		  and generates interpretable features to assist
		  radiologists' decision-making based on deep learning.
		  RATING leverages the complementary advantages of multimodal
		  ultrasound images and solves the limited training data
		  problem with self-supervised pretraining. RATING
		  outperforms all of the existing methods, achieving an
		  accuracy of 86.1\% on a prospective test dataset and 85.0\%
		  on an external test dataset. A reader study demonstrates
		  that the RATING system improves the average accuracy of 10
		  radiologists from 41.4\% to 64.0\%. As an assistive tool,
		  not only can RATING indicate the possible lesions and
		  enhance the diagnostic performance with multimodal
		  ultrasound but it can also enlighten the road to
		  human-machine collaboration in healthcare.},
  number	= {10},
  urldate	= {2025-10-09},
  journal	= {Patterns},
  author	= {Zhou, Zhanping and Zhao, Chenyang and Qiao, Hui and Wang,
		  Ming and Guo, Yuchen and Wang, Qian and Zhang, Rui and Wu,
		  Huaiyu and Dong, Fajin and Qi, Zhenhong and Li, Jianchu and
		  Tian, Xinping and Zeng, Xiaofeng and Jiang, Yuxin and Xu,
		  Feng and Dai, Qionghai and Yang, Meng},
  month		= oct,
  year		= {2022},
  keywords	= {deep learning, rheumatoid arthritis, human-machine
		  collaboration, multimodal ultrasound},
  pages		= {100592}
}

@Misc{		  zhu_medical_2024,
  title		= {Medical {SAM} 2: {Segment} medical images as video via
		  {Segment} {Anything} {Model} 2},
  shorttitle	= {Medical {SAM} 2},
  url		= {http://arxiv.org/abs/2408.00874},
  doi		= {10.48550/arXiv.2408.00874},
  abstract	= {Medical image segmentation plays a pivotal role in
		  clinical diagnostics and treatment planning, yet existing
		  models often face challenges in generalization and in
		  handling both 2D and 3D data uniformly. In this paper, we
		  introduce Medical SAM 2 (MedSAM-2), a generalized
		  auto-tracking model for universal 2D and 3D medical image
		  segmentation. The core concept is to leverage the Segment
		  Anything Model 2 (SAM2) pipeline to treat all 2D and 3D
		  medical segmentation tasks as a video object tracking
		  problem. To put it into practice, we propose a novel
		  {\textbackslash}emph\{self-sorting memory bank\} mechanism
		  that dynamically selects informative embeddings based on
		  confidence and dissimilarity, regardless of temporal order.
		  This mechanism not only significantly improves performance
		  in 3D medical image segmentation but also unlocks a
		  {\textbackslash}emph\{One-Prompt Segmentation\} capability
		  for 2D images, allowing segmentation across multiple images
		  from a single prompt without temporal relationships. We
		  evaluated MedSAM-2 on five 2D tasks and nine 3D tasks,
		  including white blood cells, optic cups, retinal vessels,
		  mandibles, coronary arteries, kidney tumors, liver tumors,
		  breast cancer, nasopharynx cancer, vestibular schwannoma,
		  mediastinal lymph nodules, cerebral artery, inferior
		  alveolar nerve, and abdominal organs, comparing it against
		  state-of-the-art (SOTA) models in task-tailored, general
		  and interactive segmentation settings. Our findings
		  demonstrate that MedSAM-2 surpasses a wide range of
		  existing models and updates new SOTA on several benchmarks.
		  The code is released on the project page:
		  https://supermedintel.github.io/Medical-SAM2/.},
  urldate	= {2025-10-07},
  publisher	= {arXiv},
  author	= {Zhu, Jiayuan and Hamdi, Abdullah and Qi, Yunli and Jin,
		  Yueming and Wu, Junde},
  month		= dec,
  year		= {2024},
  note		= {arXiv:2408.00874 [cs]},
  keywords	= {Computer Science - Computer Vision and Pattern
		  Recognition}
}
