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Medical image classification is a critical problem for healthcare, with the potential to alleviate the workload of doctors and facilitate diagnoses of patients.
Design and development of a multimodal biomedical information retrieval system
Dina Demner-Fushman, Sameer Antani, Matthew Simpson, and George R Thoma · 2012
Earlier work this paper cites.
Machine learning approaches in medical image analysis: From detection to diagnosis, 2016
Marleen De Bruijne · 2016
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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
Earlier work this paper cites.
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
Earlier work this paper cites.
Mdnet: A semantically and visually interpretable medical image diagnosis network
Zizhao Zhang, Yuanpu Xie, Fuyong Xing, Mason McGough, and Lin Yang · 2017
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Identifying medical diagnoses and treatable diseases by image-based deep learning
Daniel S Kermany, Michael Goldbaum, Wenjia Cai, Carolina CS Valentim, Huiying Liang, Sally L Baxter, Alex McKeown, Ge Yang, Xiaokang Wu, Fangbing Yan, et al · 2018
Earlier work this paper cites.
Large-scale celebfaces attributes (celeba) dataset
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2018
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Generalizing to unseen domains via adversarial data augmentation
Riccardo Volpi, Hongseok Namkoong, Ozan Sener, John Duchi, Vittorio Murino, and Silvio Savarese · 2018
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Confounding variables can degrade generalization performance of radiological deep learning models
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric K Oermann · 2018
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Global and local interpretability for cardiac mri classification
James R Clough, Ilkay Oksuz, Esther Puyol-Antón, Bram Ruijsink, Andrew P King, and Julia A Schnabel · 2019
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Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports
Alistair EW Johnson, Tom J Pollard, Seth J Berkowitz, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Roger G Mark, and Steven Horng · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
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Can ai help in screening viral and covid-19 pneumonia?
Muhammad EH Chowdhury, Tawsifur Rahman, Amith Khandakar, Rashid Mazhar, Muhammad Abdul Kadir, Zaid Bin Mahbub, Khandakar Reajul Islam, Muhammad Salman Khan, Atif Iqbal, Nasser Al Emadi, et al · 2020
Earlier work this paper cites.
Ct imaging features of 2019 novel coronavirus (2019-ncov)
Michael Chung, Adam Bernheim, Xueyan Mei, Ning Zhang, Mingqian Huang, Xianjun Zeng, Jiufa Cui, Wenjian Xu, Yang Yang, Zahi A Fayad, et al · 2020
Earlier work this paper cites.
Covid-19 image data collection
Joseph Paul Cohen, Paul Morrison, and Lan Dao · 2020
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Artificial intelligence for the detection of covid-19 pneumonia on chest ct using multinational datasets
Stephanie A Harmon, Thomas H Sanford, Sheng Xu, Evrim B Turkbey, Holger Roth, Ziyue Xu, Dong Yang, Andriy Myronenko, Victoria Anderson, Amel Amalou, et al · 2020
Earlier work this paper cites.
Concept bottleneck models
Pang Wei Koh, Thao Nguyen, Yew Siang Tang, Stephen Mussmann, Emma Pierson, Been Kim, and Percy Liang · 2020
Earlier work this paper cites.
Learning to learn single domain generalization
Fengchun Qiao, Long Zhao, and Xi Peng · 2020
Earlier work this paper cites.
Coronavirus disease 2019 (covid-19): a systematic review of imaging findings in 919 patients
Sana Salehi, Aidin Abedi, Sudheer Balakrishnan, Ali Gholamrezanezhad, et al · 2020
Earlier work this paper cites.
A review on novel coronavirus (covid-19): symptoms, transmission and diagnosis tests
Kowsar Sheikhi, Hamidreza Shirzadfar, and Milad Sheikhi · 2020
Cited alongside, same era.
Explainable deep learning models in medical image analysis
Amitojdeep Singh, Sourya Sengupta, and Vasudevan Lakshminarayanan · 2020
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Saunet: Shape attentive u-net for interpretable medical image segmentation
Jesse Sun, Fatemeh Darbehani, Mark Zaidi, and Bo Wang · 2020
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Maximum-entropy adversarial data augmentation for improved generalization and robustness
Long Zhao, Ting Liu, Xi Peng, and Dimitris Metaxas · 2020
Cited alongside, same era.
Domain generalization with optimal transport and metric learning
Fan Zhou, Zhuqing Jiang, Changjian Shui, Boyu Wang, and Brahim Chaib-draa · 2020
Cited alongside, same era.
Semi-supervised multi-label classification with 3d cbam resnet for tuberculosis cavern report
Xing Lu, An Yan, Eric Y Chang, C-n Hsu, Julian McAuley, Jiang Du, and Amilcare Gentili · 2022
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Visual classification via description from large language models
Sachit Menon and Carl Vondrick · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Why black box machine learning should be avoided for high-stakes decisions, in brief
Cynthia Rudin · 2022
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Explainable artificial intelligence (xai) in deep learning-based medical image analysis
Bas HM Van der Velden, Hugo J Kuijf, Kenneth GA Gilhuijs, and Max A Viergever · 2022
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Big self-supervised models advance medical image classification
Shekoofeh Azizi, Basil Mustafa, Fiona Ryan, Zachary Beaver, Jan Freyberg, Jonathan Deaton, Aaron Loh, Alan Karthikesalingam, Simon Kornblith, Ting Chen, et al · 2021
Cited alongside, same era.
A case-based interpretable deep learning model for classification of mass lesions in digital mammography
Alina Jade Barnett, Fides Regina Schwartz, Chaofan Tao, Chaofan Chen, Yinhao Ren, Joseph Y Lo, and Cynthia Rudin · 2021
Cited alongside, same era.
WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton Earnshaw, Imran S. Haque, Sara M. Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
Cited alongside, same era.
Public covid-19 x-ray datasets and their impact on model bias–a systematic review of a significant problem
Beatriz Garcia Santa Cruz, Matías Nicolás Bossa, Jan Sölter, and Andreas Dominik Husch · 2021
Cited alongside, same era.
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Yiqiu Shen, Nan Wu, Jason Phang, Jungkyu Park, Kangning Liu, Sudarshini Tyagi, Laura Heacock, S Gene Kim, Linda Moy, Kyunghyun Cho, et al · 2021
Cited alongside, same era.
Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip HS Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
Cited alongside, same era.
Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer
Simon M Thomas, James G Lefevre, Glenn Baxter, and Nicholas A Hamilton · 2021
Cited alongside, same era.
Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Radbert: Adapting transformer-based language models to radiology
An Yan, Julian McAuley, Xing Lu, Jiang Du, Eric Y Chang, Amilcare Gentili, and Chun-Nan Hsu · 2022
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Yue Yang, Artemis Panagopoulou, Shenghao Zhou, Daniel Jin, Chris Callison-Burch, and Mark Yatskar · 2022
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Improving out-of-distribution robustness via selective augmentation
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn · 2022
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Post-hoc concept bottleneck models
Mert Yuksekgonul, Maggie Wang, and James Zou · 2022
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Concept embedding models: Beyond the accuracy-explainability trade-off
Mateo Espinosa Zarlenga, Barbiero Pietro, Ciravegna Gabriele, Marra Giuseppe, Francesco Giannini, Michelangelo Diligenti, Shams Zohreh, Precioso Frederic, Stefano Melacci, Weller Adrian, et al · 2022
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" nothing abnormal": Disambiguating medical reports via contrastive knowledge infusion
Zexue He, An Yan, Amilcare Gentili, Julian McAuley, and Chun-Nan Hsu · 2023
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Capabilities of gpt-4 on medical challenge problems
Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan, and Eric Horvitz · 2023
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Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng · 2023
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Gpt-4 technical report
OpenAI · 2023
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Transformers in medical imaging: A survey
Fahad Shamshad, Salman Khan, Syed Waqas Zamir, Muhammad Haris Khan, Munawar Hayat, Fahad Shahbaz Khan, and Huazhu Fu · 2023
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Llama: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
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Learning concise and descriptive attributes for visual recognition
An Yan, Yu Wang, Yiwu Zhong, Chengyu Dong, Zexue He, Yujie Lu, William Yang Wang, Jingbo Shang, and Julian McAuley · 2023
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