

@article{ikezogwoQuilt1MOneMillion2023,
  title={Quilt-1m: One million image-text pairs for histopathology},
  author={Ikezogwo, Wisdom and Seyfioglu, Saygin and Ghezloo, Fatemeh and Geva, Dylan and Sheikh Mohammed, Fatwir and Anand, Pavan Kumar and Krishna, Ranjay and Shapiro, Linda},
  journal={Advances in neural information processing systems},
  volume={36},
  pages={37995--38017},
  year={2023}
}

@article{luVisualLanguageFoundationModel2023,
  title={A visual-language foundation model for computational pathology},
  author={Lu, Ming Y and Chen, Bowen and Williamson, Drew FK and Chen, Richard J and Liang, Ivy and Ding, Tong and Jaume, Guillaume and Odintsov, Igor and Le, Long Phi and Gerber, Georg and others},
  journal={Nature Medicine},
  volume={30},
  number={3},
  pages={863--874},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@article{tran2024generating,
  title={Generating highly accurate pathology reports from gigapixel whole slide images with HistoGPT},
  author={Tran, Manuel and Schmidle, Paul and Wagner, Sophia J and Koch, Valentin and Lupperger, Valerio and Feuchtinger, Annette and B{\"o}hner, Alexander and Kaczmarczyk, Robert and Biedermann, Tilo and Eyerich, Kilian and others},
  journal={medRxiv},
  pages={2024--03},
  year={2024},
  publisher={Cold Spring Harbor Laboratory Press}
}


@inproceedings{linMicrosoftCOCOCommon2015,
  title={Microsoft coco: Common objects in context},
  author={Lin, Tsung-Yi and Maire, Michael and Belongie, Serge and Hays, James and Perona, Pietro and Ramanan, Deva and Doll{\'a}r, Piotr and Zitnick, C Lawrence},
  booktitle={Computer vision--ECCV 2014: 13th European conference, zurich, Switzerland, September 6-12, 2014, proceedings, part v 13},
  pages={740--755},
  year={2014},
  organization={Springer}
}

@article{bultenArtificialIntelligenceDiagnosis2022,
  title={Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge},
  author={Bulten, Wouter and Kartasalo, Kimmo and Chen, Po-Hsuan Cameron and Str{\"o}m, Peter and Pinckaers, Hans and Nagpal, Kunal and Cai, Yuannan and Steiner, David F and Van Boven, Hester and Vink, Robert and others},
  journal={Nature medicine},
  volume={28},
  number={1},
  pages={154--163},
  year={2022},
  publisher={Nature Publishing Group US New York}
}
@incollection{chenWsiCaptionMultipleInstance2024,
	address = {Cham},
	title = {{WsiCaption}: {Multiple} {Instance} {Generation} of {Pathology} {Reports} for {Gigapixel} {Whole}-{Slide} {Images}},
	volume = {15004},
	isbn = {978-3-031-72082-6 978-3-031-72083-3},
	shorttitle = {{WsiCaption}},
	url = {https://link.springer.com/10.1007/978-3-031-72083-3_51},
	abstract = {Whole slide images are the foundation of digital pathology for the diagnosis and treatment of carcinomas. Writing pathology reports is laborious and error-prone for inexperienced pathologists. To reduce the workload and improve clinical automation, we investigate how to generate pathology reports given whole slide images. On the data end, we curated the largest WSI-text dataset (PathText). In specific, we collected nearly 10000 high-quality WSI-text pairs for visual-language models by recognizing and cleaning pathology reports which narrate diagnostic slides in TCGA. On the model end, we propose the multiple instance generative model (MI-Gen) which can produce pathology reports for gigapixel WSIs. We benchmark our model on the largest subset of PathText. Experimental results show our model can generate pathology reports which contain multiple clinical clues and achieve competitive performance on certain slide-level tasks. We observe that simple semantic extraction from the pathology reports can achieve the best performance (0.838 of F1 score) on BRCA subtyping surpassing previous state-of-theart approaches. Our collected dataset and related code are available at https://github.com/cpystan/Wsi-Caption.},
	language = {en},
	urldate = {2024-11-18},
	booktitle = {Medical {Image} {Computing} and {Computer} {Assisted} {Intervention} – {MICCAI} 2024},
	publisher = {Springer Nature Switzerland},
	author = {Chen, Pingyi and Li, Honglin and Zhu, Chenglu and Zheng, Sunyi and Shui, Zhongyi and Yang, Lin},
	editor = {Linguraru, Marius George and Dou, Qi and Feragen, Aasa and Giannarou, Stamatia and Glocker, Ben and Lekadir, Karim and Schnabel, Julia A.},
	year = {2024},
	doi = {10.1007/978-3-031-72083-3_51},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {546--556},
	file = {Chen et al. - 2024 - WsiCaption Multiple Instance Generation of Pathol.pdf:/Users/judith/Zotero/storage/BGQSBWZ5/Chen et al. - 2024 - WsiCaption Multiple Instance Generation of Pathol.pdf:application/pdf},
}
@article{zhangWhyAreVisuallyGrounded2024a,
  title={Why are visually-grounded language models bad at image classification?},
  author={Zhang, Yuhui and Unell, Alyssa and Wang, Xiaohan and Ghosh, Dhruba and Su, Yuchang and Schmidt, Ludwig and Yeung-Levy, Serena},
  journal={arXiv preprint arXiv:2405.18415},
  year={2024}
}


@article{vorontsovVirchowMillionSlideDigital2024,
  title={Virchow: A million-slide digital pathology foundation model},
  author={Vorontsov, Eugene and Bozkurt, Alican and Casson, Adam and Shaikovski, George and Zelechowski, Michal and Liu, Siqi and Severson, Kristen and Zimmermann, Eric and Hall, James and Tenenholtz, Neil and others},
  journal={arXiv preprint arXiv:2309.07778},
  year={2023}
}
@inproceedings{radfordLearningTransferableVisual2021b,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PmLR}
}
@article{shaikovskiPRISMMultiModalGenerative2024,
  title={Prism: A multi-modal generative foundation model for slide-level histopathology},
  author={Shaikovski, George and Casson, Adam and Severson, Kristen and Zimmermann, Eric and Wang, Yi Kan and Kunz, Jeremy D and Retamero, Juan A and Oakley, Gerard and Klimstra, David and Kanan, Christopher and others},
  journal={arXiv preprint arXiv:2405.10254},
  year={2024}
}


@article{chenGeneralpurposeFoundationModel2024b,
  title={Towards a general-purpose foundation model for computational pathology},
  author={Chen, Richard J and Ding, Tong and Lu, Ming Y and Williamson, Drew FK and Jaume, Guillaume and Song, Andrew H and Chen, Bowen and Zhang, Andrew and Shao, Daniel and Shaban, Muhammad and others},
  journal={Nature Medicine},
  volume={30},
  number={3},
  pages={850--862},
  year={2024},
  publisher={Nature Publishing Group US New York}
}

@article{laurenconWhatMattersWhen2024,
  title={What matters when building vision-language models?},
  author={Lauren{\c{c}}on, Hugo and Tronchon, L{\'e}o and Cord, Matthieu and Sanh, Victor},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={87874--87907},
  year={2024}
}

@inproceedings{tongEyesWideShut2024,
  title={Eyes wide shut? exploring the visual shortcomings of multimodal llms},
  author={Tong, Shengbang and Liu, Zhuang and Zhai, Yuexiang and Ma, Yi and LeCun, Yann and Xie, Saining},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9568--9578},
  year={2024}
}

@article{zhaiInvestigatingCatastrophicForgetting2023,
  title={Investigating the catastrophic forgetting in multimodal large language models},
  author={Zhai, Yuexiang and Tong, Shengbang and Li, Xiao and Cai, Mu and Qu, Qing and Lee, Yong Jae and Ma, Yi},
  journal={arXiv preprint arXiv:2309.10313},
  year={2023}
}


@inproceedings{karamchetiPrismaticVLMsInvestigating2024a,
  title={Prismatic vlms: Investigating the design space of visually-conditioned language models},
  author={Karamcheti, Siddharth and Nair, Suraj and Balakrishna, Ashwin and Liang, Percy and Kollar, Thomas and Sadigh, Dorsa},
  booktitle={Forty-first International Conference on Machine Learning},
  year={2024}
}

@article{reimersSentenceBERTSentenceEmbeddings2019a,
  title={Sentence-bert: Sentence embeddings using siamese bert-networks},
  author={Reimers, Nils and Gurevych, Iryna},
  journal={arXiv preprint arXiv:1908.10084},
  year={2019}
}

 
@article{grisiHierarchicalVisionTransformers2023,
  title={Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images},
  author={Grisi, Cl{\'e}ment and Litjens, Geert and van der Laak, Jeroen},
  journal={arXiv preprint arXiv:2312.12619},
  year={2023}
}

@article{bultenArtificialIntelligenceDiagnosis2022a,
	title = {Artificial intelligence for diagnosis and {Gleason} grading of prostate cancer: the {PANDA} challenge},
	volume = {28},
	copyright = {2022 The Author(s)},
	issn = {1546-170X},
	shorttitle = {Artificial intelligence for diagnosis and {Gleason} grading of prostate cancer},
	url = {https://www.nature.com/articles/s41591-021-01620-2},
	doi = {10.1038/s41591-021-01620-2},
	abstract = {Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95\% confidence interval (CI), 0.840–0.884) and 0.868 (95\% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.},
	language = {en},
	number = {1},
	urldate = {2025-01-13},
	journal = {Nature Medicine},
	author = {Bulten, Wouter and Kartasalo, Kimmo and Chen, Po-Hsuan Cameron and Ström, Peter and Pinckaers, Hans and Nagpal, Kunal and Cai, Yuannan and Steiner, David F. and van Boven, Hester and Vink, Robert and Hulsbergen-van de Kaa, Christina and van der Laak, Jeroen and Amin, Mahul B. and Evans, Andrew J. and van der Kwast, Theodorus and Allan, Robert and Humphrey, Peter A. and Grönberg, Henrik and Samaratunga, Hemamali and Delahunt, Brett and Tsuzuki, Toyonori and Häkkinen, Tomi and Egevad, Lars and Demkin, Maggie and Dane, Sohier and Tan, Fraser and Valkonen, Masi and Corrado, Greg S. and Peng, Lily and Mermel, Craig H. and Ruusuvuori, Pekka and Litjens, Geert and Eklund, Martin},
	month = jan,
	year = {2022},
	note = {Publisher: Nature Publishing Group},
	keywords = {Medical imaging, Machine learning, Prostate cancer},
	pages = {154--163},
	file = {Full Text PDF:/Users/judith/Zotero/storage/JNAN64VR/Bulten et al. - 2022 - Artificial intelligence for diagnosis and Gleason .pdf:application/pdf},
}

@article{bultenArtificialIntelligenceDiagnosis2022b,
	title = {Artificial intelligence for diagnosis and {Gleason} grading of prostate cancer: the {PANDA} challenge},
	volume = {28},
	issn = {1078-8956, 1546-170X},
	shorttitle = {Artificial intelligence for diagnosis and {Gleason} grading of prostate cancer},
	url = {https://www.nature.com/articles/s41591-021-01620-2},
	doi = {10.1038/s41591-021-01620-2},
	abstract = {Abstract
            Artificial intelligence (AI) has shown promise for diagnosing prostate cancer in biopsies. However, results have been limited to individual studies, lacking validation in multinational settings. Competitions have been shown to be accelerators for medical imaging innovations, but their impact is hindered by lack of reproducibility and independent validation. With this in mind, we organized the PANDA challenge—the largest histopathology competition to date, joined by 1,290 developers—to catalyze development of reproducible AI algorithms for Gleason grading using 10,616 digitized prostate biopsies. We validated that a diverse set of submitted algorithms reached pathologist-level performance on independent cross-continental cohorts, fully blinded to the algorithm developers. On United States and European external validation sets, the algorithms achieved agreements of 0.862 (quadratically weighted κ, 95\% confidence interval (CI), 0.840–0.884) and 0.868 (95\% CI, 0.835–0.900) with expert uropathologists. Successful generalization across different patient populations, laboratories and reference standards, achieved by a variety of algorithmic approaches, warrants evaluating AI-based Gleason grading in prospective clinical trials.},
	language = {en},
	number = {1},
	urldate = {2025-01-13},
	journal = {Nature Medicine},
	author = {Bulten, Wouter and Kartasalo, Kimmo and Chen, Po-Hsuan Cameron and Ström, Peter and Pinckaers, Hans and Nagpal, Kunal and Cai, Yuannan and Steiner, David F. and Van Boven, Hester and Vink, Robert and Hulsbergen-van De Kaa, Christina and Van Der Laak, Jeroen and Amin, Mahul B. and Evans, Andrew J. and Van Der Kwast, Theodorus and Allan, Robert and Humphrey, Peter A. and Grönberg, Henrik and Samaratunga, Hemamali and Delahunt, Brett and Tsuzuki, Toyonori and Häkkinen, Tomi and Egevad, Lars and Demkin, Maggie and Dane, Sohier and Tan, Fraser and Valkonen, Masi and Corrado, Greg S. and Peng, Lily and Mermel, Craig H. and Ruusuvuori, Pekka and Litjens, Geert and Eklund, Martin and {the PANDA challenge consortium} and Brilhante, Américo and Çakır, Aslı and Farré, Xavier and Geronatsiou, Katerina and Molinié, Vincent and Pereira, Guilherme and Roy, Paromita and Saile, Günter and Salles, Paulo G. O. and Schaafsma, Ewout and Tschui, Joëlle and Billoch-Lima, Jorge and Pereira, Emíio M. and Zhou, Ming and He, Shujun and Song, Sejun and Sun, Qing and Yoshihara, Hiroshi and Yamaguchi, Taiki and Ono, Kosaku and Shen, Tao and Ji, Jianyi and Roussel, Arnaud and Zhou, Kairong and Chai, Tianrui and Weng, Nina and Grechka, Dmitry and Shugaev, Maxim V. and Kiminya, Raphael and Kovalev, Vassili and Voynov, Dmitry and Malyshev, Valery and Lapo, Elizabeth and Campos, Manuel and Ota, Noriaki and Yamaoka, Shinsuke and Fujimoto, Yusuke and Yoshioka, Kentaro and Juvonen, Joni and Tukiainen, Mikko and Karlsson, Antti and Guo, Rui and Hsieh, Chia-Lun and Zubarev, Igor and Bukhar, Habib S. T. and Li, Wenyuan and Li, Jiayun and Speier, William and Arnold, Corey and Kim, Kyungdoc and Bae, Byeonguk and Kim, Yeong Won and Lee, Hong-Seok and Park, Jeonghyuk},
	month = jan,
	year = {2022},
	pages = {154--163},
	file = {Bulten et al. - 2022 - Artificial intelligence for diagnosis and Gleason .pdf:/Users/judith/Zotero/storage/YGBBCEJS/Bulten et al. - 2022 - Artificial intelligence for diagnosis and Gleason .pdf:application/pdf},
}
@article{leeBioBERTPretrainedBiomedical2020,
  title={BioBERT: a pre-trained biomedical language representation model for biomedical text mining},
  author={Lee, Jinhyuk and Yoon, Wonjin and Kim, Sungdong and Kim, Donghyeon and Kim, Sunkyu and So, Chan Ho and Kang, Jaewoo},
  journal={Bioinformatics},
  volume={36},
  number={4},
  pages={1234--1240},
  year={2020},
  publisher={Oxford University Press}
}

@inproceedings{radford2021learning,
  title={Learning transferable visual models from natural language supervision},
  author={Radford, Alec and Kim, Jong Wook and Hallacy, Chris and Ramesh, Aditya and Goh, Gabriel and Agarwal, Sandhini and Sastry, Girish and Askell, Amanda and Mishkin, Pamela and Clark, Jack and others},
  booktitle={International conference on machine learning},
  pages={8748--8763},
  year={2021},
  organization={PmLR}
}


@article{kirkpatrickOvercomingCatastrophicForgetting2017,
  title={Overcoming catastrophic forgetting in neural networks},
  author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},
  journal={Proceedings of the national academy of sciences},
  volume={114},
  number={13},
  pages={3521--3526},
  year={2017},
  publisher={National Academy of Sciences}
}
@article{joeranbosmaDragonrobertabasedomainspecific2024,
  title={The DRAGON benchmark for clinical NLP},
  author={Bosma, Joeran S and Dercksen, Koen and Builtjes, Luc and Andr{\'e}, Romain and Roest, Christian and Fransen, Stefan J and Noordman, Constant R and Navarro-Padilla, Mar and Lefkes, Judith and Alves, Nat{\'a}lia and others},
  journal={npj Digital Medicine},
  volume={8},
  number={1},
  pages={1--10},
  year={2025},
  publisher={Nature Publishing Group}
}

@article{wangPathologyFoundationModel2024a,
  title={A pathology foundation model for cancer diagnosis and prognosis prediction},
  author={Wang, Xiyue and Zhao, Junhan and Marostica, Eliana and Yuan, Wei and Jin, Jietian and Zhang, Jiayu and Li, Ruijiang and Tang, Hongping and Wang, Kanran and Li, Yu and others},
  journal={Nature},
  volume={634},
  number={8035},
  pages={970--978},
  year={2024},
  publisher={Nature Publishing Group UK London}
}

@article{chenGeneralpurposeFoundationModel2024a,
	title = {Towards a general-purpose foundation model for computational pathology},
	volume = {30},
	copyright = {2024 The Author(s), under exclusive licence to Springer Nature America, Inc.},
	issn = {1546-170X},
	url = {https://www.nature.com/articles/s41591-024-02857-3},
	doi = {10.1038/s41591-024-02857-3},
	abstract = {Quantitative evaluation of tissue images is crucial for computational pathology (CPath) tasks, requiring the objective characterization of histopathological entities from whole-slide images (WSIs). The high resolution of WSIs and the variability of morphological features present significant challenges, complicating the large-scale annotation of data for high-performance applications. To address this challenge, current efforts have proposed the use of pretrained image encoders through transfer learning from natural image datasets or self-supervised learning on publicly available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using more than 100 million images from over 100,000 diagnostic H\&amp;E-stained WSIs (\&gt;77 TB of data) across 20 major tissue types. The model was evaluated on 34 representative CPath tasks of varying diagnostic difficulty. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient artificial intelligence models that can generalize and transfer to a wide range of diagnostically challenging tasks and clinical workflows in anatomic pathology. Pretrained using over 100,000 diagnostic histopathological slides across 20 major tissue types, a self-supervised model is shown to outperform existing baselines across various clinically relevant computational pathology tasks.},
	language = {en},
	number = {3},
	urldate = {2025-01-21},
	journal = {Nature Medicine},
	author = {Chen, Richard J. and Ding, Tong and Lu, Ming Y. and Williamson, Drew F. K. and Jaume, Guillaume and Song, Andrew H. and Chen, Bowen and Zhang, Andrew and Shao, Daniel and Shaban, Muhammad and Williams, Mane and Oldenburg, Lukas and Weishaupt, Luca L. and Wang, Judy J. and Vaidya, Anurag and Le, Long Phi and Gerber, Georg and Sahai, Sharifa and Williams, Walt and Mahmood, Faisal},
	month = mar,
	year = {2024},
	note = {Publisher: Nature Publishing Group},
	keywords = {Machine learning, Pathology, Biomedical engineering},
	pages = {850--862},
	file = {Full Text PDF:/Users/judith/Zotero/storage/C4YFL4FW/Chen et al. - 2024 - Towards a general-purpose foundation model for com.pdf:application/pdf},
}
@article{wuVisualTransformersTokenbased2020,
  title={Visual transformers: Token-based image representation and processing for computer vision},
  author={Wu, Bichen and Xu, Chenfeng and Dai, Xiaoliang and Wan, Alvin and Zhang, Peizhao and Yan, Zhicheng and Tomizuka, Masayoshi and Gonzalez, Joseph and Keutzer, Kurt and Vajda, Peter},
  journal={arXiv preprint arXiv:2006.03677},
  year={2020}
}

@inproceedings{dengImageNetLargescaleHierarchical2009,
	title = {{ImageNet}: {A} large-scale hierarchical image database},
	shorttitle = {{ImageNet}},
	url = {https://ieeexplore.ieee.org/document/5206848},
	doi = {10.1109/CVPR.2009.5206848},
	abstract = {The explosion of image data on the Internet has the potential to foster more sophisticated and robust models and algorithms to index, retrieve, organize and interact with images and multimedia data. But exactly how such data can be harnessed and organized remains a critical problem. We introduce here a new database called “ImageNet”, a large-scale ontology of images built upon the backbone of the WordNet structure. ImageNet aims to populate the majority of the 80,000 synsets of WordNet with an average of 500–1000 clean and full resolution images. This will result in tens of millions of annotated images organized by the semantic hierarchy of WordNet. This paper offers a detailed analysis of ImageNet in its current state: 12 subtrees with 5247 synsets and 3.2 million images in total. We show that ImageNet is much larger in scale and diversity and much more accurate than the current image datasets. Constructing such a large-scale database is a challenging task. We describe the data collection scheme with Amazon Mechanical Turk. Lastly, we illustrate the usefulness of ImageNet through three simple applications in object recognition, image classification and automatic object clustering. We hope that the scale, accuracy, diversity and hierarchical structure of ImageNet can offer unparalleled opportunities to researchers in the computer vision community and beyond.},
	urldate = {2025-01-22},
	booktitle = {2009 {IEEE} {Conference} on {Computer} {Vision} and {Pattern} {Recognition}},
	author = {Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
	month = jun,
	year = {2009},
	note = {ISSN: 1063-6919},
	keywords = {Information retrieval, Explosions, Image databases, Image retrieval, Internet, Large-scale systems, Multimedia databases, Ontologies, Robustness, Spine},
	pages = {248--255},
	file = {Full Text PDF:/Users/judith/Zotero/storage/QHKR8U9X/Deng et al. - 2009 - ImageNet A large-scale hierarchical image databas.pdf:application/pdf;IEEE Xplore Abstract Record:/Users/judith/Zotero/storage/CQT9AIPB/5206848.html:text/html},
}


@article{ahmedPathAlignVisionlanguageModel2024,
  title={Pathalign: A vision-language model for whole slide images in histopathology},
  author={Ahmed, Faruk and Sellergren, Andrew and Yang, Lin and Xu, Shawn and Babenko, Boris and Ward, Abbi and Olson, Niels and Mohtashamian, Arash and Matias, Yossi and Corrado, Greg S and others},
  journal={arXiv preprint arXiv:2406.19578},
  year={2024}
}
@inproceedings{zhangContrastiveLearningMedical2022a,
  title={Contrastive learning of medical visual representations from paired images and text},
  author={Zhang, Yuhao and Jiang, Hang and Miura, Yasuhide and Manning, Christopher D and Langlotz, Curtis P},
  booktitle={Machine learning for healthcare conference},
  pages={2--25},
  year={2022},
  organization={PMLR}
}

@article{bandiContinualLearningStrategies2023,
  title={Continual learning strategies for cancer-independent detection of lymph node metastases},
  author={B{\'a}ndi, P{\'e}ter and Balkenhol, Maschenka and Van Dijk, Marcory and Kok, Michel and van Ginneken, Bram and van der Laak, Jeroen and Litjens, Geert},
  journal={Medical Image Analysis},
  volume={85},
  pages={102755},
  year={2023},
  publisher={Elsevier}
}
@article{perkoniggDynamicMemoryAlleviate2021,
  title={Dynamic memory to alleviate catastrophic forgetting in continual learning with medical imaging},
  author={Perkonigg, Matthias and Hofmanninger, Johannes and Herold, Christian J and Brink, James A and Pianykh, Oleg and Prosch, Helmut and Langs, Georg},
  journal={Nature communications},
  volume={12},
  number={1},
  pages={5678},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@article{kirkpatrickOvercomingCatastrophicForgetting2017a,
	title = {Overcoming catastrophic forgetting in neural networks},
	volume = {114},
	url = {https://www.pnas.org/doi/10.1073/pnas.1611835114},
	doi = {10.1073/pnas.1611835114},
	abstract = {The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.},
	number = {13},
	urldate = {2025-03-05},
	journal = {Proceedings of the National Academy of Sciences},
	author = {Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A. and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and Hassabis, Demis and Clopath, Claudia and Kumaran, Dharshan and Hadsell, Raia},
	month = mar,
	year = {2017},
	note = {Publisher: Proceedings of the National Academy of Sciences},
	pages = {3521--3526},
	file = {Full Text PDF:/Users/judith/Zotero/storage/PMFNUCC6/Kirkpatrick et al. - 2017 - Overcoming catastrophic forgetting in neural netwo.pdf:application/pdf},
}


@article{rusuProgressiveNeuralNetworks2022,
  title={Progressive neural networks},
  author={Rusu, Andrei A and Rabinowitz, Neil C and Desjardins, Guillaume and Soyer, Hubert and Kirkpatrick, James and Kavukcuoglu, Koray and Pascanu, Razvan and Hadsell, Raia},
  journal={arXiv preprint arXiv:1606.04671},
  year={2016}
}


@article{mnihHumanlevelControlDeep2015,
	title = {Human-level control through deep reinforcement learning},
	volume = {518},
	copyright = {2015 Springer Nature Limited},
	issn = {1476-4687},
	url = {https://www.nature.com/articles/nature14236},
	doi = {10.1038/nature14236},
	abstract = {An artificial agent is developed that learns to play a diverse range of classic Atari 2600 computer games directly from sensory experience, achieving a performance comparable to that of an expert human player; this work paves the way to building general-purpose learning algorithms that bridge the divide between perception and action.},
	language = {en},
	number = {7540},
	urldate = {2025-03-06},
	journal = {Nature},
	author = {Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A. and Veness, Joel and Bellemare, Marc G. and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K. and Ostrovski, Georg and Petersen, Stig and Beattie, Charles and Sadik, Amir and Antonoglou, Ioannis and King, Helen and Kumaran, Dharshan and Wierstra, Daan and Legg, Shane and Hassabis, Demis},
	month = feb,
	year = {2015},
	note = {Publisher: Nature Publishing Group},
	keywords = {Computer science},
	pages = {529--533},
	file = {Full Text PDF:/Users/judith/Zotero/storage/9T6YT43J/Mnih et al. - 2015 - Human-level control through deep reinforcement lea.pdf:application/pdf},
}
@inproceedings{rebuffiICaRLIncrementalClassifier2017,
  title={icarl: Incremental classifier and representation learning},
  author={Rebuffi, Sylvestre-Alvise and Kolesnikov, Alexander and Sperl, Georg and Lampert, Christoph H},
  booktitle={Proceedings of the IEEE conference on Computer Vision and Pattern Recognition},
  pages={2001--2010},
  year={2017}
}

@inproceedings{schroffFaceNetUnifiedEmbedding2015,
  title={Facenet: A unified embedding for face recognition and clustering},
  author={Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={815--823},
  year={2015}
}

