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In the context of medical artificial intelligence, this study explores the vulnerabilities of the Pathology Language-Image Pretraining (PLIP) model, a Vision Language Foundation model, under targeted attacks.
“Explaining and harnessing adversarial examples,”
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy, · 2014
Earlier work this paper cites.
“Towards evaluating the robustness of neural networks,”
Nicholas Carlini and David Wagner, · 2017
Earlier work this paper cites.
“Towards deep learning models resistant to adversarial attacks,” 2019
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu, · 2019
Earlier work this paper cites.
“Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study,”
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A Valous, Dyke Ferber, et al., · 2019
Earlier work this paper cites.
“Pannuke: an open pan-cancer histology dataset for nuclei instance segmentation and classification,”
Jevgenij Gamper, Navid Alemi Koohbanani, Ksenija Benet, Ali Khuram, and Nasir Rajpoot, · 2019
Earlier work this paper cites.
“Ai-based pathology predicts origins for cancers of unknown primary,”
Ming Y Lu, Tiffany Y Chen, Drew FK Williamson, Melissa Zhao, Maha Shady, Jana Lipkova, and Faisal Mahmood, · 2021
Cited alongside, same era.
“Deep learning-based transformation of h&e stained tissues into special stains,”
Kevin de Haan, Yijie Zhang, Jonathan E Zuckerman, Tairan Liu, Anthony E Sisk, Miguel FP Diaz, Kuang-Yu Jen, Alexander Nobori, Sofia Liou, Sarah Zhang, et al., · 2021
Cited alongside, same era.
“To be robust or to be fair: Towards fairness in adversarial training,”
Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang, · 2021
Cited alongside, same era.
“Online adversarial purification based on self-supervised learning,”
Changhao Shi, Chester Holtz, and Gal Mishne, · 2021
Cited alongside, same era.
“Digestpath: A benchmark dataset with challenge review for the pathological detection and segmentation of digestive-system,”
Qian Da, Xiaodi Huang, Zhongyu Li, Yanfei Zuo, Chenbin Zhang, Jingxin Liu, Wen Chen, Jiahui Li, Dou Xu, Zhiqiang Hu, et al., · 2022
Cited alongside, same era.
“Vl-interpret: An interactive visualization tool for interpreting vision-language transformers,”
Estelle Aflalo, Meng Du, Shao-Yen Tseng, Yongfei Liu, Chenfei Wu, Nan Duan, and Vasudev Lal, · 2022
Later among the works it cites.
“Diffusion models for adversarial purification,”
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar, · 2022
Later among the works it cites.
“Prospective implementation of ai-assisted screen reading to improve early detection of breast cancer,”
Annie Y Ng, Cary JG Oberije, Éva Ambrózay, Endre Szabó, Orsolya Serfőző, Edit Karpati, Georgia Fox, Ben Glocker, Elizabeth A Morris, Gábor Forrai, et al., · 2023
Later among the works it cites.
“A visual–language foundation model for pathology image analysis using medical twitter,”
Z. Huang, F. Bianchi, M. Yuksekgonul, T. J. Montine, and J. Zou, · 2023
Later among the works it cites.
“Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs,” 2024
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Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, Cliff Wong, Andrea Tupini, Yu Wang, Matt Mazzola, Swadheen Shukla, Lars Liden, Jianfeng Gao, Matthew P. Lungren, Tristan Naumann, Sheng Wang, and Hoifung Poon, · 2024
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