@article{Zhang2025RadIR,
  title  = {RadIR: A Scalable Framework for Multi‑Grained Medical Image Retrieval via Radiology Report Mining},
  author = {Zhang, Tengfei and Zhao, Ziheng and Wu, Chaoyi and Zhou, Xiao and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
  journal = {arXiv preprint arXiv:2503.04653},
  year   = {2025},
  note   = {Version 2, submitted 12 Jul 2025},
  url    = {https://arxiv.org/abs/2503.04653}
}

@article{Lee2023RegionMIR,
  title  = {Region‑Based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query},
  author = {Lee, Ho Hin and Santamaria‑Pang, Alberto and Merkow, Jameson and Oktay, Ozan and Pérez‑García, Fernando and Alvarez‑Valle, Javier and Tarapov, Ivan},
  journal = {arXiv preprint arXiv:2305.05598},
  year   = {2023},
  url    = {https://arxiv.org/abs/2305.05598}
}

@ARTICLE{Muller2004-bn,
  title    = "A review of content-based image retrieval systems in medical
              applications-clinical benefits and future directions",
  author   = "M{\"u}ller, Henning and Michoux, Nicolas and Bandon, David and
              Geissbuhler, Antoine",
  abstract = "Content-based visual information retrieval (CBVIR) or
              content-based image retrieval (CBIR) has been one on the most
              vivid research areas in the field of computer vision over the
              last 10 years. The availability of large and steadily growing
              amounts of visual and multimedia data, and the development of the
              Internet underline the need to create thematic access methods
              that offer more than simple text-based queries or requests based
              on matching exact database fields. Many programs and tools have
              been developed to formulate and execute queries based on the
              visual or audio content and to help browsing large multimedia
              repositories. Still, no general breakthrough has been achieved
              with respect to large varied databases with documents of
              differing sorts and with varying characteristics. Answers to many
              questions with respect to speed, semantic descriptors or
              objective image interpretations are still unanswered. In the
              medical field, images, and especially digital images, are
              produced in ever-increasing quantities and used for diagnostics
              and therapy. The Radiology Department of the University Hospital
              of Geneva alone produced more than 12,000 images a day in 2002.
              The cardiology is currently the second largest producer of
              digital images, especially with videos of cardiac catheterization
              ( approximately 1800 exams per year containing almost 2000 images
              each). The total amount of cardiologic image data produced in the
              Geneva University Hospital was around 1 TB in 2002. Endoscopic
              videos can equally produce enormous amounts of data. With digital
              imaging and communications in medicine (DICOM), a standard for
              image communication has been set and patient information can be
              stored with the actual image(s), although still a few problems
              prevail with respect to the standardization. In several articles,
              content-based access to medical images for supporting clinical
              decision-making has been proposed that would ease the management
              of clinical data and scenarios for the integration of
              content-based access methods into picture archiving and
              communication systems (PACS) have been created. This article
              gives an overview of available literature in the field of
              content-based access to medical image data and on the
              technologies used in the field. Section 1 gives an introduction
              into generic content-based image retrieval and the technologies
              used. Section 2 explains the propositions for the use of image
              retrieval in medical practice and the various approaches. Example
              systems and application areas are described. Section 3 describes
              the techniques used in the implemented systems, their datasets
              and evaluations. Section 4 identifies possible clinical benefits
              of image retrieval systems in clinical practice as well as in
              research and education. New research directions are being defined
              that can prove to be useful. This article also identifies
              explanations to some of the outlined problems in the field as it
              looks like many propositions for systems are made from the
              medical domain and research prototypes are developed in computer
              science departments using medical datasets. Still, there are very
              few systems that seem to be used in clinical practice. It needs
              to be stated as well that the goal is not, in general, to replace
              text-based retrieval methods as they exist at the moment but to
              complement them with visual search tools.",
  journal  = "Int J Med Inform",
  volume   =  73,
  number   =  1,
  pages    = "1--23",
  month    =  feb,
  year     =  2004,
  address  = "Ireland",
  language = "en"
}

@ARTICLE{Choe2021-nh,
  title    = "Content-based Image Retrieval by Using Deep Learning for
              Interstitial Lung Disease Diagnosis with Chest {CT}",
  author   = "Choe, Jooae and Hwang, Hye Jeon and Seo, Joon Beom and Lee, Sang
              Min and Yun, Jihye and Kim, Min-Ju and Jeong, Jewon and Lee,
              Youngsoo and Jin, Kiok and Park, Rohee and Kim, Jihoon and Jeon,
              Howook and Kim, Namkug and Yi, Jaeyoun and Yu, Donghoon and Kim,
              Byeongsoo",
  abstract = "Background Evaluation of interstitial lung disease (ILD) at CT is
              a challenging task that requires experience and is subject to
              substantial interreader variability. Purpose To investigate
              whether a proposed content-based image retrieval (CBIR) of
              similar chest CT images by using deep learning can aid in the
              diagnosis of ILD by readers with different levels of experience.
              Materials and Methods This retrospective study included patients
              with confirmed ILD after multidisciplinary discussion and
              available CT images identified between January 2000 and December
              2015. Database was composed of four disease classes: usual
              interstitial pneumonia (UIP), nonspecific interstitial pneumonia
              (NSIP), cryptogenic organizing pneumonia, and chronic
              hypersensitivity pneumonitis. Eighty patients were selected as
              queries from the database. The proposed CBIR retrieved the top
              three similar CT images with diagnosis from the database by
              comparing the extent and distribution of different regional
              disease patterns quantified by a deep learning algorithm. Eight
              readers with varying experience interpreted the query CT images
              and provided their most probable diagnosis in two reading
              sessions 2 weeks apart, before and after applying CBIR.
              Diagnostic accuracy was analyzed by using McNemar test and
              generalized estimating equation, and interreader agreement was
              analyzed by using Fleiss $\kappa$. Results A total of 288
              patients were included (mean age, 58 years $\pm$ 11 [standard
              deviation]; 145 women). After applying CBIR, the overall
              diagnostic accuracy improved in all readers (before CBIR, 46.1\%
              [95\% CI: 37.1, 55.3]; after CBIR, 60.9\% [95\% CI: 51.8, 69.3];
              P < .001). In terms of disease category, the diagnostic accuracy
              improved after applying CBIR in UIP (before vs after CBIR, 52.4\%
              vs 72.8\%, respectively; P < .001) and NSIP cases (before vs
              after CBIR, 42.9\% vs 61.6\%, respectively; P < .001).
              Interreader agreement improved after CBIR (before vs after CBIR
              Fleiss $\kappa$, 0.32 vs 0.47, respectively; P = .005).
              Conclusion The proposed content-based image retrieval system for
              chest CT images with deep learning improved the diagnostic
              accuracy of interstitial lung disease and interreader agreement
              in readers with different levels of experience. \copyright{}
              RSNA, 2021 Online supplemental material is available for this
              article. See also the editorial by Wielp{\"u}tz in this issue.",
  journal  = "Radiology",
  volume   =  302,
  number   =  1,
  pages    = "187--197",
  month    =  oct,
  year     =  2021,
  address  = "United States",
  language = "en"
}

@article{QAYYUM20178,
title = {Medical image retrieval using deep convolutional neural network},
journal = {Neurocomputing},
volume = {266},
pages = {8-20},
year = {2017},
issn = {0925-2312},
doi = {https://doi.org/10.1016/j.neucom.2017.05.025},
url = {https://www.sciencedirect.com/science/article/pii/S0925231217308445},
author = {Adnan Qayyum and Syed Muhammad Anwar and Muhammad Awais and Muhammad Majid},
keywords = {Content based medical image retrieval (CBMIR), Convolutional neural networks (CNNs), Similarity metric, Deep learning},
abstract = {With a widespread use of digital imaging data in hospitals, the size of medical image repositories is increasing rapidly. This causes difficulty in managing and querying these large databases leading to the need of content based medical image retrieval (CBMIR) systems. A major challenge in CBMIR systems is the semantic gap that exists between the low level visual information captured by imaging devices and high level semantic information perceived by human. The efficacy of such systems is more crucial in terms of feature representations that can characterize the high-level information completely. In this paper, we propose a framework of deep learning for CBMIR system by using deep convolutional neural network (CNN) that is trained for classification of medical images. An intermodal dataset that contains twenty-four classes and five modalities is used to train the network. The learned features and the classification results are used to retrieve medical images. For retrieval, best results are achieved when class based predictions are used. An average classification accuracy of 99.77% and a mean average precision of 0.69 is achieved for retrieval task. The proposed method is best suited to retrieve multimodal medical images for different body organs.}
}

@article{10.1109/TCSVT.2021.3080920,
author = {Dubey, Shiv Ram},
title = {A Decade Survey of Content Based Image Retrieval Using Deep Learning},
year = {2022},
issue_date = {May 2022},
publisher = {IEEE Press},
volume = {32},
number = {5},
issn = {1051-8215},
url = {https://doi.org/10.1109/TCSVT.2021.3080920},
doi = {10.1109/TCSVT.2021.3080920},
abstract = {The content based image retrieval aims to find the similar images from a large scale dataset against a query image. Generally, the similarity between the representative features of the query image and dataset images is used to rank the images for retrieval. In early days, various hand designed feature descriptors have been investigated based on the visual cues such as color, texture, shape, etc. that represent the images. However, the deep learning has emerged as a dominating alternative of hand-designed feature engineering from a decade. It learns the features automatically from the data. This paper presents a comprehensive survey of deep learning based developments in the past decade for content based image retrieval. The categorization of existing state-of-the-art methods from different perspectives is also performed for greater understanding of the progress. The taxonomy used in this survey covers different supervision, different networks, different descriptor type and different retrieval type. A performance analysis is also performed using the state-of-the-art methods. The insights are also presented for the benefit of the researchers to observe the progress and to make the best choices. The survey presented in this paper will help in further research progress in image retrieval using deep learning.},
journal = {IEEE Trans. Cir. and Sys. for Video Technol.},
month = may,
pages = {2687–2704},
numpages = {18}
}

@INPROCEEDINGS{9706900,
  author={Hu, Brian and Vasu, Bhavan and Hoogs, Anthony},
  booktitle={2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)}, 
  title={X-MIR: EXplainable Medical Image Retrieval}, 
  year={2022},
  volume={},
  number={},
  pages={1544-1554},
  keywords={COVID-19;Measurement;Computer vision;Image retrieval;Medical services;Machine learning;Skin;Explainable AI;Fairness;Accountability;Privacy and Ethics in Vision Medical Imaging/Imaging for Bioinformatics/Biological and Cell Microscopy},
  doi={10.1109/WACV51458.2022.00161}}

@article{OZTURK2021102601,
title = {Class-driven content-based medical image retrieval using hash codes of deep features},
journal = {Biomedical Signal Processing and Control},
volume = {68},
pages = {102601},
year = {2021},
issn = {1746-8094},
doi = {https://doi.org/10.1016/j.bspc.2021.102601},
url = {https://www.sciencedirect.com/science/article/pii/S1746809421001981},
author = {Şaban Öztürk},
keywords = {Image retrieval, CBMIR, Hashing, Deep features, CT, MRI},
abstract = {Medical imaging provides the convenience of physicians to analyze the disease by providing visual data of the body parts required for clinical research and treatment. Today, increasing medical images following technological developments are stored for a better understanding of diseases and future diagnoses. Effective medical image indexing and retrieval systems are required to use these images from storage repositories in real-time. In this quest, this paper provides an effective indexing and retrieval framework using deep features for MR and CT image indexing and searching. The proposed system aims to produce the most effective and least parameterized hash codes by using image features. For this reason, deep features are obtained from medical images using the convolutional neural network (CNN) architecture, which is the most effective automatic feature extraction method. The length of the acquired raw deep feature vectors for an image is relatively inefficient for retrieval speed. Feature reduction methods are used for the most effective reduction of the length of the deep feature vector. The most effective feature reduction algorithm is determined in this study. The main reason for producing a reduced class-driven hash code with feature selection algorithms is the drawbacks of medical image datasets. These drawbacks prevent the CNN output from being used directly as hash-code. The performance of the proposed method is tested on NEMA MRI and NEMA CT datasets. The proposed method is able to outperform the other state-of-the-art algorithms in terms of average precision performance.}
}

@ARTICLE{Yan2018-jb,
  title    = "{DeepLesion}: automated mining of large-scale lesion annotations
              and universal lesion detection with deep learning",
  author   = "Yan, Ke and Wang, Xiaosong and Lu, Le and Summers, Ronald M",
  abstract = "Extracting, harvesting, and building large-scale annotated
              radiological image datasets is a greatly important yet
              challenging problem. Meanwhile, vast amounts of clinical
              annotations have been collected and stored in hospitals' picture
              archiving and communication systems (PACS). These types of
              annotations, also known as bookmarks in PACS, are usually marked
              by radiologists during their daily workflow to highlight
              significant image findings that may serve as reference for later
              studies. We propose to mine and harvest these abundant
              retrospective medical data to build a large-scale lesion image
              dataset. Our process is scalable and requires minimum manual
              annotation effort. We mine bookmarks in our institute to develop
              DeepLesion, a dataset with 32,735 lesions in 32,120 CT slices
              from 10,594 studies of 4,427 unique patients. There are a variety
              of lesion types in this dataset, such as lung nodules, liver
              tumors, enlarged lymph nodes, and so on. It has the potential to
              be used in various medical image applications. Using DeepLesion,
              we train a universal lesion detector that can find all types of
              lesions with one unified framework. In this challenging task, the
              proposed lesion detector achieves a sensitivity of 81.1\% with
              five false positives per image.",
  journal  = "J Med Imaging (Bellingham)",
  volume   =  5,
  number   =  3,
  pages    = "036501",
  month    =  jul,
  year     =  2018,
  address  = "United States",
  keywords = "bookmark; convolutional neural network; deep learning; lesion
              detection; medical image dataset; picture archiving and
              communication system",
  language = "en"
}

@article{ZHONG2021101993,
title = {Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19},
journal = {Medical Image Analysis},
volume = {70},
pages = {101993},
year = {2021},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.101993},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521000396},
author = {Aoxiao Zhong and Xiang Li and Dufan Wu and Hui Ren and Kyungsang Kim and Younggon Kim and Varun Buch and Nir Neumark and Bernardo Bizzo and Won Young Tak and Soo Young Park and Yu Rim Lee and Min Kyu Kang and Jung Gil Park and Byung Seok Kim and Woo Jin Chung and Ning Guo and Ittai Dayan and Mannudeep K. Kalra and Quanzheng Li},
keywords = {Chest radiograph, COVID-19, Image retrieval, Image content query},
abstract = {In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019 (COVID-19) pandemic. Chest radiograph (CXR) has been playing a crucial role in COVID-19 patient triaging, diagnosing and monitoring, particularly in the United States. Considering the mixed and unspecific signals in CXR, an image retrieval model of CXR that provides both similar images and associated clinical information can be more clinically meaningful than a direct image diagnostic model. In this work we develop a novel CXR image retrieval model based on deep metric learning. Unlike traditional diagnostic models which aim at learning the direct mapping from images to labels, the proposed model aims at learning the optimized embedding space of images, where images with the same labels and similar contents are pulled together. The proposed model utilizes multi-similarity loss with hard-mining sampling strategy and attention mechanism to learn the optimized embedding space, and provides similar images, the visualizations of disease-related attention maps and useful clinical information to assist clinical decisions. The model is trained and validated on an international multi-site COVID-19 dataset collected from 3 different sources. Experimental results of COVID-19 image retrieval and diagnosis tasks show that the proposed model can serve as a robust solution for CXR analysis and patient management for COVID-19. The model is also tested on its transferability on a different clinical decision support task for COVID-19, where the pre-trained model is applied to extract image features from a new dataset without any further training. The extracted features are then combined with COVID-19 patient's vitals, lab tests and medical histories to predict the possibility of airway intubation in 72 hours, which is strongly associated with patient prognosis, and is crucial for patient care and hospital resource planning. These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.}
}

@misc{abacha20233dmir,
      title={3D-MIR: A Benchmark and Empirical Study on 3D Medical Image Retrieval in Radiology}, 
      author={Asma Ben Abacha and Alberto Santamaria-Pang and Ho Hin Lee and Jameson Merkow and Qin Cai and Surya Teja Devarakonda and Abdullah Islam and Julia Gong and Matthew P. Lungren and Thomas Lin and Noel C Codella and Ivan Tarapov},
      year={2023},
      eprint={2311.13752},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2311.13752}, 
}

@ARTICLE{Johnson2019-bq,
  title    = "{MIMIC-CXR}, a de-identified publicly available database of chest
              radiographs with free-text reports",
  author   = "Johnson, Alistair E W and Pollard, Tom J and Berkowitz, Seth J
              and Greenbaum, Nathaniel R and Lungren, Matthew P and Deng,
              Chih-Ying and Mark, Roger G and Horng, Steven",
  abstract = "Chest radiography is an extremely powerful imaging modality,
              allowing for a detailed inspection of a patient's chest, but
              requires specialized training for proper interpretation. With the
              advent of high performance general purpose computer vision
              algorithms, the accurate automated analysis of chest radiographs
              is becoming increasingly of interest to researchers. Here we
              describe MIMIC-CXR, a large dataset of 227,835 imaging studies
              for 65,379 patients presenting to the Beth Israel Deaconess
              Medical Center Emergency Department between 2011--2016. Each
              imaging study can contain one or more images, usually a frontal
              view and a lateral view. A total of 377,110 images are available
              in the dataset. Studies are made available with a semi-structured
              free-text radiology report that describes the radiological
              findings of the images, written by a practicing radiologist
              contemporaneously during routine clinical care. All images and
              reports have been de-identified to protect patient privacy. The
              dataset is made freely available to facilitate and encourage a
              wide range of research in computer vision, natural language
              processing, and clinical data mining.",
  journal  = "Scientific Data",
  volume   =  6,
  number   =  1,
  pages    = "317",
  month    =  dec,
  year     =  2019
}

@misc{lee2023regionbased,
      title={Region-based Contrastive Pretraining for Medical Image Retrieval with Anatomic Query}, 
      author={Ho Hin Lee and Alberto Santamaria-Pang and Jameson Merkow and Ozan Oktay and Fernando Pérez-García and Javier Alvarez-Valle and Ivan Tarapov},
      year={2023},
      eprint={2305.05598},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2305.05598}, 
}

@InProceedings{ZhaTen_RadIR_MICCAI2025,
        author = { Zhang, Tengfei AND Zhao, Ziheng AND Wu, Chaoyi AND Zhou, Xiao AND Zhang, Ya AND Wang, Yanfeng AND Xie, Weidi},
        title = { { RadIR: A Scalable Framework for Multi-Grained Medical Image Retrieval via Radiology Report Mining } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2025},
        year = {2025},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15964},
        month = {September},
        page = {508 -- 518}
}

@ARTICLE{Nagy2022-ko,
  title    = "A pediatric wrist trauma X-ray dataset ({GRAZPEDWRI-DX}) for
              machine learning",
  author   = "Nagy, Eszter and Janisch, Michael and Hr{\v z}i{\'c}, Franko and
              Sorantin, Erich and Tschauner, Sebastian",
  abstract = "Digital radiography is widely available and the standard modality
              in trauma imaging, often enabling to diagnose pediatric wrist
              fractures. However, image interpretation requires time-consuming
              specialized training. Due to astonishing progress in computer
              vision algorithms, automated fracture detection has become a
              topic of research interest. This paper presents the GRAZPEDWRI-DX
              dataset containing annotated pediatric trauma wrist radiographs
              of 6,091 patients, treated at the Department for Pediatric
              Surgery of the University Hospital Graz between 2008 and 2018. A
              total number of 10,643 studies (20,327 images) are made
              available, typically covering posteroanterior and lateral
              projections. The dataset is annotated with 74,459 image tags and
              features 67,771 labeled objects. We de-identified all radiographs
              and converted the DICOM pixel data to 16-Bit grayscale PNG
              images. The filenames and the accompanying text files provide
              basic patient information (age, sex). Several pediatric
              radiologists annotated dataset images by placing lines, bounding
              boxes, or polygons to mark pathologies like fractures or
              periosteal reactions. They also tagged general image
              characteristics. This dataset is publicly available to encourage
              computer vision research.",
  journal  = "Scientific Data",
  volume   =  9,
  number   =  1,
  pages    = "222",
  month    =  may,
  year     =  2022
}

@INPROCEEDINGS{109340,
  author={Pizer, S.M. and Johnston, R.E. and Ericksen, J.P. and Yankaskas, B.C. and Muller, K.E.},
  booktitle={[1990] Proceedings of the First Conference on Visualization in Biomedical Computing}, 
  title={Contrast-limited adaptive histogram equalization: speed and effectiveness}, 
  year={1990},
  volume={},
  number={},
  pages={337-345},
  keywords={Histograms;Adaptive equalizers;Biomedical imaging;Computed tomography;Displays;Lesions;Lungs;Clinical diagnosis;Design methodology;Medical diagnostic imaging},
  doi={10.1109/VBC.1990.109340}}

@software{Jocher_Ultralytics_YOLO_2023,
author = {Jocher, Glenn and Qiu, Jing and Chaurasia, Ayush},
license = {AGPL-3.0},
month = jan,
title = {{Ultralytics YOLO}},
url = {https://github.com/ultralytics/ultralytics},
version = {8.0.0},
year = {2023}
}

@article{langlotz2006radlex,
  title={RadLex: a new method for indexing online educational materials},
  author={Langlotz, Curtis P and others},
  journal={RadioGraphics},
  volume={26},
  number={6},
  pages={1595--1597},
  year={2006},
  publisher={Radiological Society of North America}
}

@software{pydantic,
  title = {Pydantic},
  author = {Samuel Colvin},
  year = {2017},
  url = {https://github.com/pydantic/pydantic},
  note = {Python data validation using Python type annotations}
}
@misc{sellergren2025medgemmatechnicalreport,
      title={MedGemma Technical Report}, 
      author={Andrew Sellergren and Sahar Kazemzadeh and Tiam Jaroensri and Atilla Kiraly and Madeleine Traverse and Timo Kohlberger and Shawn Xu and Fayaz Jamil and Cían Hughes and Charles Lau and Justin Chen and Fereshteh Mahvar and Liron Yatziv and Tiffany Chen and Bram Sterling and Stefanie Anna Baby and Susanna Maria Baby and Jeremy Lai and Samuel Schmidgall and Lu Yang and Kejia Chen and Per Bjornsson and Shashir Reddy and Ryan Brush and Kenneth Philbrick and Mercy Asiedu and Ines Mezerreg and Howard Hu and Howard Yang and Richa Tiwari and Sunny Jansen and Preeti Singh and Yun Liu and Shekoofeh Azizi and Aishwarya Kamath and Johan Ferret and Shreya Pathak and Nino Vieillard and Ramona Merhej and Sarah Perrin and Tatiana Matejovicova and Alexandre Ramé and Morgane Riviere and Louis Rouillard and Thomas Mesnard and Geoffrey Cideron and Jean-bastien Grill and Sabela Ramos and Edouard Yvinec and Michelle Casbon and Elena Buchatskaya and Jean-Baptiste Alayrac and Dmitry Lepikhin and Vlad Feinberg and Sebastian Borgeaud and Alek Andreev and Cassidy Hardin and Robert Dadashi and Léonard Hussenot and Armand Joulin and Olivier Bachem and Yossi Matias and Katherine Chou and Avinatan Hassidim and Kavi Goel and Clement Farabet and Joelle Barral and Tris Warkentin and Jonathon Shlens and David Fleet and Victor Cotruta and Omar Sanseviero and Gus Martins and Phoebe Kirk and Anand Rao and Shravya Shetty and David F. Steiner and Can Kirmizibayrak and Rory Pilgrim and Daniel Golden and Lin Yang},
      year={2025},
      eprint={2507.05201},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2507.05201}, 
}

@misc{zhang2025biomedclip,
      title={BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs}, 
      author={Sheng Zhang and Yanbo Xu and Naoto Usuyama and Hanwen Xu and Jaspreet Bagga and Robert Tinn and Sam Preston and Rajesh Rao and Mu Wei and Naveen Valluri and Cliff Wong and Andrea Tupini and Yu Wang and Matt Mazzola and Swadheen Shukla and Lars Liden and Jianfeng Gao and Angela Crabtree and Brian Piening and Carlo Bifulco and Matthew P. Lungren and Tristan Naumann and Sheng Wang and Hoifung Poon},
      year={2025},
      eprint={2303.00915},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2303.00915}, 
}

@misc{oord2019representationlearningcontrastivepredictive,
      title={Representation Learning with Contrastive Predictive Coding}, 
      author={Aaron van den Oord and Yazhe Li and Oriol Vinyals},
      year={2019},
      eprint={1807.03748},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/1807.03748}, 
}

@misc{wang2022medclip,
      title={MedCLIP: Contrastive Learning from Unpaired Medical Images and Text}, 
      author={Zifeng Wang and Zhenbang Wu and Dinesh Agarwal and Jimeng Sun},
      year={2022},
      eprint={2210.10163},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2210.10163}, 
}

@inproceedings{lin2023pmcclip,
author = {Lin, Weixiong and Zhao, Ziheng and Zhang, Xiaoman and Wu, Chaoyi and Zhang, Ya and Wang, Yanfeng and Xie, Weidi},
title = {PMC-CLIP: Contrastive Language-Image Pre-training Using Biomedical Documents},
year = {2023},
isbn = {978-3-031-43992-6},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-031-43993-3_51},
doi = {10.1007/978-3-031-43993-3_51},
abstract = {Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address this issue, we build and release PMC-OA, a biomedical dataset with 1.6M image-caption pairs collected from PubMedCentral’s OpenAccess subset, which is 8 times larger than before, PMC-OA covers diverse modalities or diseases, with majority of the image-caption samples aligned at finer-grained level, i.e., subfigure and subcaption. While pretraining a CLIP-style model on PMC-OA, our model named PMC-CLIP outperform previous state-of-the-art models on various downstream tasks, including image-text retrieval on ROCO, MedMNIST image classification, Medical VQA, for example, +8.1\% R@10 on image-text retrieval, +3.9\% accuracy on image classification.},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2023: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VIII},
pages = {525–536},
numpages = {12},
keywords = {Multimodal Dataset, Vision-Language Pretraining},
location = {Vancouver, BC, Canada}
}

@inproceedings{
vasylechko2025enhancing,
title={Enhancing Wrist Fracture Detection through {LLM}-Powered Data Extraction and Knowledge-Based Ensemble Learning},
author={Serge Didenko Vasylechko and Andy Tsai and Onur Afacan and Sila Kurugol},
booktitle={Medical Imaging with Deep Learning},
year={2025},
url={https://openreview.net/forum?id=Rd93NNCOnW}
}

@software{ilharco_gabriel_2021_5143773,
  author       = {Ilharco, Gabriel and
                  Wortsman, Mitchell and
                  Wightman, Ross and
                  Gordon, Cade and
                  Carlini, Nicholas and
                  Taori, Rohan and
                  Dave, Achal and
                  Shankar, Vaishaal and
                  Namkoong, Hongseok and
                  Miller, John and
                  Hajishirzi, Hannaneh and
                  Farhadi, Ali and
                  Schmidt, Ludwig},
  title        = {OpenCLIP},
  month        = jul,
  year         = 2021,
  publisher    = {Zenodo},
  version      = {0.1},
  doi          = {10.5281/zenodo.5143773},
  url          = {https://doi.org/10.5281/zenodo.5143773}
}

@InProceedings{Yan_2024_CVPR,
    author    = {Yan, Sixing and Cheung, William K. and Tsang, Ivor W. and Chiu, Keith and Tong, Terence M. and Cheung, Ka Chun and See, Simon},
    title     = {AHIVE: Anatomy-aware Hierarchical Vision Encoding for Interactive Radiology Report Retrieval},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {14324-14333}
}

@InProceedings{Aka_CheXtriev_MICCAI2024,
        author = { Akash R.  J., Naren and Tadanki, Arihanth and Sivaswamy, Jayanthi},
        title = { { CheXtriev: Anatomy-Centered Representation for Case-Based Retrieval of Chest Radiographs } },
        booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
        year = {2024},
        publisher = {Springer Nature Switzerland},
        volume = {LNCS 15001},
        month = {October},
        page = {461 -- 471}
}