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Fine-tuning pre-trained Vision-Language Models (VLMs) has shown remarkable capabilities in medical image and textual depiction synergy.
Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, Jayne Seekins, David A. Mong, Safwan S. Halabi, Jesse K. Sandberg, Ricky Jones, David B. Larson, Curtis P. Langlotz, Bhavik N. Patel, Matthew P. Lungren, and Andrew Y. Ng. 2019 · 1901
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Theoretically principled trade-off between robustness and accuracy
Hongyang Zhang, Yaodong Yu, Jiantao Jiao, Eric P. Xing, Laurent El Ghaoui, and Michael I. Jordan. 2019 · 1901
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Adversarial training for free!
Ali Shafahi, Mahyar Najibi, Amin Ghiasi, Zheng Xu, John Dickerson, Christoph Studer, Larry S. Davis, Gavin Taylor, and Tom Goldstein. 2019 · 1904
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Lxmert: Learning cross-modality encoder representations from transformers
Hao Tan and Mohit Bansal. 2019 · 1908
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The use of the area under the roc curve in the evaluation of machine learning algorithms
Andrew P. Bradley. 1997 · 1997
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Normalized loss functions for deep learning with noisy labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey. 2020 · 2006
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Dual t: Reducing estimation error for transition matrix in label-noise learning
Yu Yao, Tongliang Liu, Bo Han, Mingming Gong, Jiankang Deng, Gang Niu, and Masashi Sugiyama. 2021 · 2006
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Learning from noisy labels with deep neural networks: A survey
Hwanjun Song, Minseok Kim, Dongmin Park, Yooju Shin, and Jae-Gil Lee. 2022 · 2007
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, and Jiliang Tang. 2021 · 2010
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Multimodal deep learning
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee, and Andrew Y Ng. 2011 · 2011
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and P. S. Sastry. 2017 · 2017
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Training deep neural-networks using a noise adaptation layer
Jacob Goldberger and Ehud Ben-Reuven. 2017 · 2017
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Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases
Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, and Ronald M. Summers. 2017 · 2017
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A dataset of clinically generated visual questions and answers about radiology images
Jason J Lau, Soumya Gayen, Asma Ben Abacha, and Dina Demner-Fushman. 2018 · 2018
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Adversarial attacks on medical machine learning
Samuel G. Finlayson, John D. Bowers, Joichi Ito, Jonathan L. Zittrain, Andrew L. Beam, and Isaac S. Kohane. 2019 · 2019
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Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel. 2019 · 2019
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Hyperspectral image classification in the presence of noisy labels
Junjun Jiang, Jiayi Ma, Zheng Wang, Chen Chen, and Xianming Liu. 2019 · 2019
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2019 · 2019
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Are anchor points really indispensable in label-noise learning?
Xiaobo Xia, Tongliang Liu, Nannan Wang, Bo Han, Chen Gong, Gang Niu, and Masashi Sugiyama. 2019 · 2019
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Mitigating adversarial attacks on medical image understanding systems
Rahul Paul, Matthew Schabath, Robert Gillies, Lawrence Hall, and Dmitry Goldgof. 2020 · 2020
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MedICaT: A Dataset of Medical Images, Captions, and Textual References
Sanjay Subramanian, Sachin Mehta Lucy Lu Wang, Madeleine van Zuylen Ben Bogin, Sravanthi Parasa, Matt Gardner Sameer Singh, and Hannaneh Hajishirzi. 2020 · 2020
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Recent advances in adversarial training for adversarial robustness
Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021 · 2021
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Adversarial attack vulnerability of medical image analysis systems: Unexplored factors
Gerda Bortsova, Cristina González-Gonzalo, Suzanne C. Wetstein, Florian Dubost, Ioannis Katramados, Laurens Hogeweg, Bart Liefers, Bram van Ginneken, Josien P.W. Pluim, Mitko Veta, Clara I. Sánchez, and Marleen de Bruijne. 2021 · 2021
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Slake: A semantically-labeled knowledge-enhanced dataset for medical visual question answering
Bo Liu, Li-Ming Zhan, Li Xu, Lin Ma, Yan Yang, and Xiao-Ming Wu. 2021 · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, Gretchen Krueger, and Ilya Sutskever. 2021 · 2021
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PubMedCLIP: How much does CLIP benefit visual question answering in the medical domain?
Sedigheh Eslami, Christoph Meinel, and Gerard de Melo. 2023 · 2023
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Datacomp: In search of the next generation of multimodal datasets
Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, and Ludwig Schmidt. 2023 · 2023
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Ehrsql: A practical text-to-sql benchmark for electronic health records
Gyubok Lee, Hyeonji Hwang, Seongsu Bae, Yeonsu Kwon, Woncheol Shin, Seongjun Yang, Minjoon Seo, Jong-Yeup Kim, and Edward Choi. 2023 · 2023
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Llava-med: Training a large language-and-vision assistant for biomedicine in one day
Chunyuan Li, Cliff Wong, Sheng Zhang, Naoto Usuyama, Haotian Liu, Jianwei Yang, Tristan Naumann, Hoifung Poon, and Jianfeng Gao. 2023 · 2023
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Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever. 2021 · 2021
Cited alongside, same era.
Vicreg: Variance-invariance-covariance regularization for self-supervised learning
Adrien Bardes, Jean Ponce, and Yann LeCun. 2022 · 2022
Cited alongside, same era.
Reproducible scaling laws for contrastive language-image learning
Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, and Jenia Jitsev. 2022 · 2022
Cited alongside, same era.
An empirical study of training end-to-end vision-and-language transformers
Zi-Yi Dou, Yichong Xu, Zhe Gan, Jianfeng Wang, Shuohang Wang, Lijuan Wang, Chenguang Zhu, Pengchuan Zhang, Lu Yuan, Nanyun Peng, Zicheng Liu, and Michael Zeng. 2022 · 2022
Cited alongside, same era.
Unsupervised domain adaptation for segmentation with black-box source model
Xiaofeng Liu, Chaehwa Yoo, Fangxu Xing, C-C Jay Kuo, Georges El Fakhri, Je-Won Kang, and Jonghye Woo. 2022 · 2022
Cited alongside, same era.
Multi-modal understanding and generation for medical images and text via vision-language pre-training
Jong Hak Moon, Hyungyung Lee, Woncheol Shin, Young-Hak Kim, and Edward Choi. 2022 · 2022
Cited alongside, same era.
Diffusion models for adversarial purification
Weili Nie, Brandon Guo, Yujia Huang, Chaowei Xiao, Arash Vahdat, and Anima Anandkumar. 2022 · 2022
Cited alongside, same era.
Guided diffusion model for adversarial purification
Jinyi Wang, Zhaoyang Lyu, Dahua Lin, Bo Dai, and Hongfei Fu. 2022 · 2022
Cited alongside, same era.
Later among the works it cites.
Understanding zero-shot adversarial robustness for large-scale models
Chengzhi Mao, Scott Geng, Junfeng Yang, Xin Wang, and Carl Vondrick. 2023 · 2023
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An llm can fool itself: A prompt-based adversarial attack
Xilie Xu, Keyi Kong, Ning Liu, Lizhen Cui, Di Wang, Jingfeng Zhang, and Mohan Kankanhalli. 2023 · 2023
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CXR-CLIP: Toward Large Scale Chest X-ray Language-Image Pre-training , page 101–111
Kihyun You, Jawook Gu, Jiyeon Ham, Beomhee Park, Jiho Kim, Eun K. Hong, Woonhyuk Baek, and Byungseok Roh. 2023 · 2023
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On evaluating adversarial robustness of large vision-language models
Yunqing Zhao, Tianyu Pang, Chao Du, Xiao Yang, Chongxuan Li, Ngai-Man Cheung, and Min Lin. 2023 · 2023
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Minigpt-4: Enhancing vision-language understanding with advanced large language models
Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2023 · 2023
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Understanding and mitigating the label noise in pre-training on downstream tasks
Hao Chen, Jindong Wang, Ankit Shah, Ran Tao, Hongxin Wei, Xing Xie, Masashi Sugiyama, and Bhiksha Raj. 2024 · 2024
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Understanding the effect of noise in llm training data with algorithmic chains of thought
Alex Havrilla and Maia Iyer. 2024 · 2024
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Advlora: Adversarial low-rank adaptation of vision-language models
Yuheng Ji, Yue Liu, Zhicheng Zhang, Zhao Zhang, Yuting Zhao, Gang Zhou, Xingwei Zhang, Xinwang Liu, and Xiaolong Zheng. 2024 · 2024
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Machine learning for synthetic data generation: A review
Yingzhou Lu, Minjie Shen, Huazheng Wang, Xiao Wang, Capucine van Rechem, Tianfan Fu, and Wenqi Wei. 2024 · 2024
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