@inproceedings{koh2020concept,
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  booktitle={International conference on machine learning},
  pages={5338--5348},
  year={2020},
  organization={PMLR}
}

@inproceedings{mehta2018net,
  title={Y-Net: joint segmentation and classification for diagnosis of breast biopsy images},
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  year={2018},
  organization={Springer}
}

@inproceedings{suris2023vipergpt,
  title={Vipergpt: Visual inference via python execution for reasoning},
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  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
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  year={2023}
}

@inproceedings{koh2021wilds,
  title={Wilds: A benchmark of in-the-wild distribution shifts},
  author={Koh, Pang Wei and Sagawa, Shiori and Marklund, Henrik and Xie, Sang Michael and Zhang, Marvin and Balsubramani, Akshay and Hu, Weihua and Yasunaga, Michihiro and Phillips, Richard Lanas and Gao, Irena and others},
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  organization={PMLR}
}

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}

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@misc{nguyen2025knockoutsimplewayhandle,
      title={Knockout: A simple way to handle missing inputs}, 
      author={Minh Nguyen and Batuhan K. Karaman and Heejong Kim and Alan Q. Wang and Fengbei Liu and Mert R. Sabuncu},
      year={2025},
      eprint={2405.20448},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
}

@article{yuksekgonul2022post,
  title={Post-hoc concept bottleneck models},
  author={Yuksekgonul, Mert and Wang, Maggie and Zou, James},
  journal={arXiv preprint arXiv:2205.15480},
  year={2022}
}

@inproceedings{goyal2019counterfactual,
  title={Counterfactual visual explanations},
  author={Goyal, Yash and Wu, Ziyan and Ernst, Jan and Batra, Dhruv and Parikh, Devi and Lee, Stefan},
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  pages={2376--2384},
  year={2019},
  organization={PMLR}
}

@article{oikarinen2023label,
  title={Label-free concept bottleneck models},
  author={Oikarinen, Tuomas and Das, Subhro and Nguyen, Lam M and Weng, Tsui-Wei},
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  year={2023}
}

@article{xu2025graph,
  title={Graph concept bottleneck models},
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  year={2025}
}

@article{kim2023probabilistic,
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  year={2023}
}

@article{espinosa2022concept,
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  year={2022}
}

@article{srivastava2024vlg,
  title={Vlg-cbm: Training concept bottleneck models with vision-language guidance},
  author={Srivastava, Divyansh and Yan, Ge and Weng, Lily},
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  volume={37},
  pages={79057--79094},
  year={2024}
}

@article{yan2023robust,
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  year={2023}
}

@inproceedings{yang2023language,
  title={Language in a bottle: Language model guided concept bottlenecks for interpretable image classification},
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  year={2023}
}

@article{liu20242024,
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  publisher={IEEE}
}

@inproceedings{gao2024aligning,
  title={Aligning human knowledge with visual concepts towards explainable medical image classification},
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  organization={Springer}
}

@article{patricio2025two,
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  year={2025},
  publisher={Elsevier}
}

@article{wong2018building,
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}

@article{hooper2023case,
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  year={2023}
}

@article{gao2025medical,
  title={Medical Image Segmentation: A Comprehensive Review of Deep Learning-Based Methods},
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  journal={Tomography},
  volume={11},
  number={5},
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  year={2025},
  publisher={MDPI}
}

@article{rayed2024deep,
  title={Deep learning for medical image segmentation: State-of-the-art advancements and challenges},
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  year={2024},
  publisher={Elsevier}
}

@inproceedings{hu2024visual,
  title={Visual program distillation: Distilling tools and programmatic reasoning into vision-language models},
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  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9590--9601},
  year={2024}
}

@article{lu2025rsvp,
  title={RSVP: Reasoning Segmentation via Visual Prompting and Multi-modal Chain-of-Thought},
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  journal={arXiv preprint arXiv:2506.04277},
  year={2025}
}

@inproceedings{gupta2023visual,
  title={Visual programming: Compositional visual reasoning without training},
  author={Gupta, Tanmay and Kembhavi, Aniruddha},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={14953--14962},
  year={2023}
}

@article{sellergren2025medgemma,
  title={Medgemma technical report},
  author={Sellergren, Andrew and Kazemzadeh, Sahar and Jaroensri, Tiam and Kiraly, Atilla and Traverse, Madeleine and Kohlberger, Timo and Xu, Shawn and Jamil, Fayaz and Hughes, C{\'\i}an and Lau, Charles and others},
  journal={arXiv preprint arXiv:2507.05201},
  year={2025}
}

@inproceedings{wang2022medclip,
  title={Medclip: Contrastive learning from unpaired medical images and text},
  author={Wang, Zifeng and Wu, Zhenbang and Agarwal, Dinesh and Sun, Jimeng},
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  volume={2022},
  pages={3876},
  year={2022}
}

@article{zhang2024biomedclip,
  title={A Multimodal Biomedical Foundation Model Trained from Fifteen Million 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},
  journal={NEJM AI},
  year={2024},
  volume={2},
  number={1},
}

@article{shi2025multimodal,
  title={A multimodal visual--language foundation model for computational ophthalmology},
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  year={2025},
  publisher={Nature Publishing Group UK London}
}

@article{ryu2025vision,
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  year={2025},
  publisher={Springer}
}

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  year={2019},
  organization={PMLR}
}

@article{chen2025review,
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}

@article{mienye2025deep,
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  year={2025},
  publisher={Mdpi}
}

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  year={2021}
}

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  year={2017}
}

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  year={2016}
}

@inproceedings{huang2017densely,
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  pages={4700--4708},
  year={2017}
}

@inproceedings{szegedy2015going,
  title={Going deeper with convolutions},
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@article{howard2017mobilenets,
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}

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  year={2023}
}

@inproceedings{hsu2025makes,
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  organization={International Conference on Learning Representations (ICLR)}
}

@misc{ronneberger2015unetconvolutionalnetworksbiomedical,
      title={U-Net: Convolutional Networks for Biomedical Image Segmentation}, 
      author={Olaf Ronneberger and Philipp Fischer and Thomas Brox},
      year={2015},
      eprint={1505.04597},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
}

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  journal = {Medical Image Analysis},
  volume  = {91},
  pages   = {103042},
  year    = {2024},
  issn    = {1361-8415},
}

@InProceedings{Pan_Integrating_MICCAI2024,
  author    = {Pang, Winnie and Ke, Xueyi and Tsutsui, Satoshi and Wen, Bihan},
  title     = {{Integrating Clinical Knowledge into Concept Bottleneck Models}},
  booktitle = {Proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
  year      = {2024},
  publisher = {Springer Nature Switzerland},
  volume    = {LNCS 15004},
  month     = {October},
  pages     = {243--253},
}

@article{kim2023concept,
  title   = {Concept Bottleneck with Visual Concept Filtering for Explainable Medical Image Classification},
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  journal = {arXiv preprint arXiv:2308.11920},
  year    = {2023},
  note    = {Accepted to MedAGI Workshop at MICCAI 2023}
}

@inproceedings{sun2017revisiting,
  title        = {Revisiting the Unreasonable Effectiveness of Data in Deep Learning Era},
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  booktitle    = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
  pages        = {843--852},
  year         = {2017},
}

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@inproceedings{marques2012role,
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@article{dosovitskiy2021an,
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  year    = {2021},
}

@inproceedings{touvron2021training,
  title     = {Training data-efficient image transformers \& distillation through attention},
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  year      = {2021},
}

@inproceedings{liu2021swin,
  title     = {Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
  author    = {Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
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  year      = {2021},
}

@inproceedings{bello2022rethinking,
  title     = {Rethinking pre-training and self-training},
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  year      = {2022}
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@article{graham2019hover,
  title={Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images},
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  pages={101563},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{koh2017understanding,
  title     = {Understanding Black-box Predictions via Influence Functions},
  author    = {Koh, Pang Wei and Liang, Percy},
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  year      = {2017},
  volume    = {70},
  series    = {Proceedings of Machine Learning Research},
  pages     = {1885--1894},
  publisher = {PMLR}
}

@article{litjens2018camelyon,
    author = {Litjens, Geert and Bandi, Peter and Ehteshami Bejnordi, Babak and Geessink, Oscar and Balkenhol, Maschenka and Bult, Peter and Halilovic, Altuna and Hermsen, Meyke and van de Loo, Rob and Vogels, Rob and Manson, Quirine F and Stathonikos, Nikolas and Baidoshvili, Alexi and van Diest, Paul and Wauters, Carla and van Dijk, Marcory and van der Laak, Jeroen},
    title = {1399 H\&amp;E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset},
    journal = {GigaScience},
    volume = {7},
    number = {6},
    pages = {giy065},
    year = {2018},
    month = {05},
    abstract = {The presence of lymph node metastases is one of the most important factors in breast cancer prognosis. The most common way to assess regional lymph node status is the sentinel lymph node procedure. The sentinel lymph node is the most likely lymph node to contain metastasized cancer cells and is excised, histopathologically processed, and examined by a pathologist. This tedious examination process is time-consuming and can lead to small metastases being missed. However, recent advances in whole-slide imaging and machine learning have opened an avenue for analysis of digitized lymph node sections with computer algorithms. For example, convolutional neural networks, a type of machine-learning algorithm, can be used to automatically detect cancer metastases in lymph nodes with high accuracy. To train machine-learning models, large, well-curated datasets are needed.We released a dataset of 1,399 annotated whole-slide images (WSIs) of lymph nodes, both with and without metastases, in 3 terabytes of data in the context of the CAMELYON16 and CAMELYON17 Grand Challenges. Slides were collected from five medical centers to cover a broad range of image appearance and staining variations. Each WSI has a slide-level label indicating whether it contains no metastases, macro-metastases, micro-metastases, or isolated tumor cells. Furthermore, for 209 WSIs, detailed hand-drawn contours for all metastases are provided. Last, open-source software tools to visualize and interact with the data have been made available.A unique dataset of annotated, whole-slide digital histopathology images has been provided with high potential for re-use.},
    issn = {2047-217X},
    eprint = {https://academic.oup.com/gigascience/article-pdf/7/6/giy065/60709038/gigascience_7_6_giy065.pdf},
}

@article{li2023llavamed,
  title={Llava-med: Training a large language-and-vision assistant for biomedicine in one day},
  author={Li, Chunyuan and Wong, Cliff and Zhang, Sheng and Usuyama, Naoto and Liu, Haotian and Yang, Jianwei and Naumann, Tristan and Poon, Hoifung and Gao, Jianfeng},
  journal={arXiv preprint arXiv:2306.00890},
  year={2023}
}


@inproceedings{codella2018skin,
  title={Skin lesion analysis toward melanoma detection: A challenge at the 2017 international symposium on biomedical imaging (isbi), hosted by the international skin imaging collaboration (isic)},
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  pages={168--172},
  year={2018},
  organization={IEEE}
}

@article{Kawahara2019,
author = {Kawahara, Jeremy and Hamarneh, Ghassan},
doi = {10.1109/JBHI.2018.2831680},
issn = {21682194},
journal = {IEEE Journal of Biomedical and Health Informatics},
number = {2},
pages = {578--585},
title = {Fully convolutional neural networks to detect clinical dermoscopic features},
volume = {23},
year = {2019}
}

@article{team2025gemma,
  title={Gemma 3 technical report},
  author={Kamath, Aishwarya and Ferret, Johan and Pathak, Shreya and Vieillard, Nino and Merhej, Ramona and Perrin, Sarah and Matejovicova, Tatiana and Ram{\'e}, Alexandre and Rivi{\`e}re, Morgane and others},
  journal={arXiv preprint arXiv:2503.19786},
  year={2025}
}