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The emergence of vision-language models has transformed medical AI, enabling unprecedented advances in diagnostic capability and clinical applications.
Epiluminescence microscopy-based classification of pigmented skin lesions using computerized image analysis and an artificial neural network
Michael Binder, Heinz Kittler, A Seeber, A Steiner, H Pehamberger, and K Wolff · 1998
Earlier work this paper cites.
Assessing diagnostic skill in dermatology: a comparison between general practitioners and dermatologists
Hue Tran, Keng Chen, Adrian C Lim, James Jabbour, and Stephen Shumack · 2005
Earlier work this paper cites.
Dermatology
Otto Braun-Falco, Gerd Plewig, Helmut H Wolff, and Richard K Winkelmann · 2013
Earlier work this paper cites.
The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions
Roderick J Hay, Nicole E Johns, Hywel C Williams, Ian W Bolliger, Robert P Dellavalle, David J Margolis, Robin Marks, Luigi Naldi, Martin A Weinstock, Sarah K Wulf, et al · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
A benchmark for automatic visual classification of clinical skin disease images
Xiaoxiao Sun, Jufeng Yang, Ming Sun, and Kai Wang · 2016
Earlier work this paper cites.
Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
Earlier work this paper cites.
Dermatologist-level classification of skin cancer with deep neural networks
Andre Esteva, Brett Kuprel, Roberto A Novoa, Justin Ko, Susan M Swetter, Helen M Blau, and Sebastian Thrun · 2017
Earlier work this paper cites.
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)
Noel CF Codella, David Gutman, M Emre Celebi, Brian Helba, Michael A Marchetti, Stephen W Dusza, Aadi Kalloo, Konstantinos Liopyris, Nabin Mishra, Harald Kittler, et al · 2018
Earlier work this paper cites.
An open-source speaker gender detection framework for monitoring gender equality
David Doukhan, Jean Carrive, Félicien Vallet, Anthony Larcher, and Sylvain Meignier · 2018
Earlier work this paper cites.
Seven-point checklist and skin lesion classification using multitask multimodal neural nets
Jeremy Kawahara, Sara Daneshvar, Giuseppe Argenziano, and Ghassan Hamarneh · 2018
Earlier work this paper cites.
The ham10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions
Philipp Tschandl, Cliff Rosendahl, and Harald Kittler · 2018
Earlier work this paper cites.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Earlier work this paper cites.
Biomedbert: A pre-trained biomedical language model for qa and ir
Souradip Chakraborty, Ekaba Bisong, Shweta Bhatt, Thomas Wagner, Riley Elliott, and Francesco Mosconi · 2020
Earlier work this paper cites.
Dermatologist-level classification of malignant lip diseases using a deep convolutional neural network
SI Cho, S Sun, J-H Mun, C Kim, SY Kim, S Cho, SW Youn, HC Kim, and JH Chung · 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.
A deep learning system for differential diagnosis of skin diseases
Yuan Liu, Ayush Jain, Clara Eng, David H Way, Kang Lee, Peggy Bui, Kimberly Kanada, Guilherme de Oliveira Marinho, Jessica Gallegos, Sara Gabriele, et al · 2020
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Pad-ufes-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones
Andre G.C. Pacheco et al · 2020
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Evaluating deep neural networks trained on clinical images in dermatology with the fitzpatrick 17k dataset
Matthew Groh, Caleb Harris, Luis Soenksen, Felix Lau, Rachel Han, Aerin Kim, Arash Koochek, and Omar Badri · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Cited alongside, same era.
Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, and et al · 2021
Cited alongside, same era.
Sigmoid loss for language image pre-training
Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, and Lucas Beyer · 2023
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Vision–language foundation model for echocardiogram interpretation
Matthew Christensen, Milos Vukadinovic, Neal Yuan, and David Ouyang · 2024
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Describing differences in image sets with natural language
Lisa Dunlap, Yuhui Zhang, Xiaohan Wang, Ruiqi Zhong, Trevor Darrell, Jacob Steinhardt, Joseph E Gonzalez, and Serena Yeung-Levy · 2024
Later among the works it cites.
Ming Hu, Kun Yuan, Yaling Shen, Feilong Tang, Xiaohao Xu, Lin Zhou, Wei Li, Ying Chen, Zhongxing Xu, Zelin Peng, et al · 2024
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Quilt-1m: One million image-text pairs for histopathology
Wisdom Ikezogwo, Saygin Seyfioglu, Fatemeh Ghezloo, Dylan Geva, Fatwir Sheikh Mohammed, Pavan Kumar Anand, Ranjay Krishna, and Linda Shapiro · 2024
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Skincon: A skin disease dataset densely annotated by domain experts for fine-grained debugging and analysis
Roxana Daneshjou, Mert Yuksekgonul, Zhuo Ran Cai, Roberto Novoa, and James Y Zou · 2022
Cited alongside, same era.
Domino: Discovering systematic errors with cross-modal embeddings
Sabri Eyuboglu, Maya Varma, Khaled Saab, Jean-Benoit Delbrouck, Christopher Lee-Messer, Jared Dunnmon, James Zou, and Christopher Ré · 2022
Cited alongside, same era.
Coca: Contrastive captioners are image-text foundation models
Jiahui Yu, Zirui Wang, Vijay Vasudevan, Legg Yeung, Mojtaba Seyedhosseini, and Yonghui Wu · 2022
Cited alongside, same era.
Dermnet, 2023
Dermnet · 2023
Cited alongside, same era.
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
Cited alongside, same era.
Pmc-clip: Contrastive language-image pre-training using biomedical documents
Weixiong Lin, Ziheng Zhao, Xiaoman Zhang, Chaoyi Wu, Ya Zhang, Yanfeng Wang, and Weidi Xie · 2023
Cited alongside, same era.
Label-free concept bottleneck models
Tuomas Oikarinen, Subhro Das, Lam M Nguyen, and Tsui-Wei Weng · 2023
Cited alongside, same era.
Skin disease classification dataset, 2024
Khushbu and Sharun Akter · 2024
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Transparent medical image ai via an image–text foundation model grounded in medical literature
Chanwoo Kim, Soham U Gadgil, Alex J DeGrave, Jesutofunmi A Omiye, Zhuo Ran Cai, Roxana Daneshjou, and Su-In Lee · 2024
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A visual-language foundation model for computational pathology
Ming Y Lu, Bowen Chen, Drew FK Williamson, Richard J Chen, Ivy Liang, Tong Ding, Guillaume Jaume, Igor Odintsov, Long Phi Le, Georg Gerber, et al · 2024
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Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Yue Shen, Huanyu Li, Can Sun, Hongtao Ji, Daojun Zhang, Kun Hu, Yiqi Tang, Yu Chen, Zikun Wei, and Junwei Lv · 2024
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Creating an empirical dermatology dataset through crowdsourcing with web search advertisements
Abbi Ward, Jimmy Li, Julie Wang, and et al · 2024
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Medtrinity-25m: A large-scale multimodal dataset with multigranular annotations for medicine
Yunfei Xie, Ce Zhou, Lang Gao, Juncheng Wu, Xianhang Li, Hong-Yu Zhou, Sheng Liu, Lei Xing, James Zou, Cihang Xie, et al · 2024
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A general-purpose multimodal foundation model for dermatology
Siyuan Yan, Zhen Yu, Clare Primiero, Cristina Vico-Alonso, Zhonghua Wang, Litao Yang, Philipp Tschandl, Ming Hu, Gin Tan, Vincent Tang, et al · 2024
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A generalist vision–language foundation model for diverse biomedical tasks
Kai Zhang, Rong Zhou, Eashan Adhikarla, Zhiling Yan, Yixin Liu, Jun Yu, Zhengliang Liu, Xun Chen, Brian D Davison, Hui Ren, et al · 2024
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Skincap: A multi-modal dermatology dataset annotated with rich medical captions
Juexiao Zhou, Liyuan Sun, Yan Xu, Wenbin Liu, Shawn Afvari, Zhongyi Han, Jiaoyan Song, Yongzhi Ji, Xiaonan He, and Xin Gao · 2024
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A multimodal biomedical foundation model trained from fifteen million image–text pairs
Sheng Zhang, Yanbo Xu, Naoto Usuyama, Hanwen Xu, Jaspreet Bagga, Robert Tinn, Sam Preston, Rajesh Rao, Mu Wei, Naveen Valluri, et al · 2025
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