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Medicine is inherently multimodal, with rich data modalities spanning text, imaging, genomics, and more.
“Language models are few-shot learners”
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry and Amanda Askell · 1901
Earlier work this paper cites.
“Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma”
Saud. AlDubayan, Jake. Conway, Sabrina. Camp, Leora Witkowski, Eric Kofman, Brendan Reardon, Seunghun Han, Nicholas Moore, Haitham Elmarakeby, Keyan Salari, Hani Choudhry, Abdullah. Al-Rubaish, Abdulsalam. Al-Sulaiman, Amein. Al-Ali, Amaro Taylor-Weiner and Eliezer. Allen · 1957
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“Multitask learning”
Rich Caruana · 1997
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“Lifelong learning algorithms”
Sebastian Thrun · 1998
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“Bleu: a method for automatic evaluation of machine translation”
Kishore Papineni, Salim Roukos, Todd Ward and Wei-Jing Zhu · 2002
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“Rouge: A package for automatic evaluation of summaries”
Chin-Yew Lin · 2004
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“A fast learning algorithm for deep belief nets”
Geoffrey Hinton, Simon Osindero and Yee-Whye Teh · 2006
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“Greedy layer-wise training of deep networks”
Yoshua Bengio, Pascal Lamblin, Dan Popovici and Hugo Larochelle · 2006
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“Extracting and composing robust features with denoising autoencoders”
Pascal Vincent, Hugo Larochelle, Yoshua Bengio and Pierre-Antoine Manzagol · 2008
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“A unified architecture for natural language processing: Deep neural networks with multitask learning”
Ronan Collobert and Jason Weston · 2008
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“Imagenet: A large-scale hierarchical image database”
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li and Li Fei-Fei · 2009
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“Multimodal deep learning”
Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee and Andrew Ng · 2011
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“A framework for variation discovery and genotyping using next-generation DNA sequencing data”
Mark DePristo, Eric Banks, Ryan Poplin, Kiran Garimella, Jared Maguire, Christopher Hartl and et al · 2011
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“Deep learning of representations for unsupervised and transfer learning”
Yoshua Bengio · 2012
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“Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration”
H. Thorvaldsdottir, J.. Robinson and J.. Mesirov · 2012
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“Two public chest X-ray datasets for computer-aided screening of pulmonary diseases”
Stefan Jaeger, Sema Candemir, Sameer Antani, Yı̀-Xiáng Wáng, Pu-Xuan Lu and George Thoma · 2014
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“Referral interventions from primary to specialist care: a systematic review of international evidence”
Lindsay Blank, Susan Baxter, Helen Woods, Elizabeth Goyder, Andrew Lee, Nick Payne and Melanie Rimmer · 2014
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“Cider: Consensus-based image description evaluation”
Ramakrishna Vedantam, C Lawrence and Devi Parikh · 2015
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“Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs”
Varun Gulshan, Lily Peng, Marc Coram, Martin Stumpe, Derek Wu, Arunachalam Narayanaswamy, Subhashini Venugopalan, Kasumi Widner, Tom Madams and Jorge Cuadros · 2016
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“YFCC100M: The new data in multimedia research”
Bart Thomee, David Shamma, Gerald Friedland, Benjamin Elizalde, Karl Ni, Douglas Poland, Damian Borth and Li-Jia Li · 2016
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“MIMIC-III, a freely accessible critical care database”
Alistair Johnson, Tom Pollard, Lu Shen, Li-wei Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony and Roger Mark · 2016
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“Extensive sequencing of seven human genomes to characterize benchmark reference materials”
Justin. Zook, David Catoe, Jennifer McDaniel, Lindsay Vang, Noah Spies and et al · 2016
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“Dermatologist-level classification of skin cancer with deep neural networks”
Andre Esteva, Brett Kuprel, Roberto Novoa, Justin Ko, Susan Swetter, Helen Blau and Sebastian Thrun · 2017
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“An overview of multi-task learning in deep neural networks”
Sebastian Ruder · 2017
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“Attention is all you need”
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan Gomez, Łukasz Kaiser and Illia Polosukhin · 2017
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“Revisiting unreasonable effectiveness of data in deep learning era”
Chen Sun, Abhinav Shrivastava, Saurabh Singh and Abhinav Gupta · 2017
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“Making the v in vqa matter: Elevating the role of image understanding in visual question answering”
Yash Goyal, Tejas Khot, Douglas Summers-Stay, Dhruv Batra and Devi Parikh · 2017
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“A curated mammography data set for use in computer-aided detection and diagnosis research”
Rebecca Lee, Francisco Gimenez, Assaf Hoogi, Kanae Miyake, Mia Gorovoy and Daniel Rubin · 2017
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“Adafactor: Adaptive learning rates with sublinear memory cost”
Noam Shazeer and Mitchell Stern · 2018
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“A universal SNP and small-indel variant caller using deep neural networks”
Ryan Poplin, Pi-Chuan Chang, David Alexander, Scott Schwartz, Thomas Colthurst, Alexander Ku, Dan Newburger, Jojo Dijamco, Nam Nguyen, Pegah Afshar, Sam Gross, Lizzie Dorfman, Cory McLean and Mark DePristo · 2018
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“A dataset of clinically generated visual questions and answers about radiology images”
Jason Lau, Soumya Gayen, Asma Ben and Dina Demner-Fushman · 2018
Earlier work this paper cites.
“A clinically applicable approach to continuous prediction of future acute kidney injury”
Nenad Tomašev, Xavier Glorot, Jack Rae, Michal Zielinski, Harry Askham, Andre Saraiva, Anne Mottram, Clemens Meyer, Suman Ravuri and Ivan Protsyuk · 2019
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“Language models are unsupervised multitask learners”
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei and Ilya Sutskever · 2019
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“Ok-vqa: A visual question answering benchmark requiring external knowledge”
Kenneth Marino, Mohammad Rastegari, Ali Farhadi and Roozbeh Mottaghi · 2019
Earlier work this paper cites.
“PubMedQA: A dataset for biomedical research question answering”
Qiao Jin, Bhuwan Dhingra, Zhengping Liu, William Cohen and Xinghua Lu · 2019
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“MIMIC-CXR, a de-identified publicly available database of chest radiographs with free-text reports”
Alistair Johnson, Tom Pollard, Seth Berkowitz, Nathaniel Greenbaum, Matthew Lungren, Chih-ying Deng, Roger Mark and Steven Horng · 2019
Earlier work this paper cites.
“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 and Katie Shpanskaya · 2019
Earlier work this paper cites.
“Clinically accurate chest x-ray report generation”
Guanxiong Liu, Tzu-Ming Hsu, Matthew McDermott, Willie Boag, Wei-Hung Weng, Peter Szolovits and Marzyeh Ghassemi · 2019
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“International evaluation of an AI system for breast cancer screening”
Scott McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg Corrado and Ara Darzi · 2020
Cited alongside, same era.
“Scaling laws for neural language models”
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu and Dario Amodei · 2020
Cited alongside, same era.
“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold and Sylvain Gelly · 2020
Cited alongside, same era.
“Improving factual completeness and consistency of image-to-text radiology report generation”
Yasuhide Miura, Yuhao Zhang, Emily Tsai, Curtis Langlotz and Dan Jurafsky · 2020
Cited alongside, same era.
“Evaluating progress in automatic chest x-ray radiology report generation”
Feiyang Yu, Mark Endo, Rayan Krishnan, Ian Pan, Andy Tsai, Eduardo Reis, Eduardo Fonseca, Henrique Lee, Zahra Abad and Andrew Ng · 2022
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“Improving chest X-Ray report generation by leveraging warm-starting”
Aaron Nicolson, Jason Dowling and Bevan Koopman · 2022
Later among the works it cites.
“Effective utilization of multiple convolutional neural networks for chest X-ray classification”
Ravidu Rammuni and Pumudu Fernando · 2022
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“Effect of Random Histogram Equalization on Breast Calcification Analysis Using Deep Learning”
Adarsh Panambur, Prathmesh Madhu and Andreas Maier · 2022
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“Retrieval of Soft Prompt Enhances Zero-Shot Task Generalization”
Seonghyeon Ye, Joel Jang, Doyoung Kim, Yongrae Jo and Minjoon Seo · 2022
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“PAD-UFES-20: A skin lesion dataset composed of patient data and clinical images collected from smartphones”
Andre Pacheco, Gustavo Lima, Amanda Salomao, Breno Krohling, Igor Biral, Gabriel de Angelo, Fábio Alves, José Esgario, Alana Simora and Pedro Castro · 2020
Cited alongside, same era.
“Randaugment: Practical automated data augmentation with a reduced search space”
Ekin Cubuk, Barret Zoph, Jonathon Shlens and Quoc Le · 2020
Cited alongside, same era.
“Pathvqa: 30000+ questions for medical visual question answering”
Xuehai He, Yichen Zhang, Luntian Mou, Eric Xing and Pengtao Xie · 2020
Cited alongside, same era.
“Generating radiology reports via memory-driven transformer”
Zhihong Chen, Yan Song, Tsung-Hui Chang and Xiang Wan · 2020
Cited alongside, same era.
Akshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek, Andrew Ng and Matthew Lungren · 2020
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“On the opportunities and risks of foundation models”
Rishi Bommasani, Drew Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael Bernstein, Jeannette Bohg, Antoine Bosselut and Emma Brunskill · 2021
Cited alongside, same era.
“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
Cited alongside, same era.
“Perceiver: General perception with iterative attention”
Andrew Jaegle, Felix Gimeno, Andy Brock, Oriol Vinyals, Andrew Zisserman and Joao Carreira · 2021
Cited alongside, same era.
Later among the works it cites.
“The future of general practice in England”
Martin Marshall · 2022
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“MedMCQA: A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering”
Ankit Pal, Logesh Umapathi and Malaikannan Sankarasubbu · 2022
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“ViLMedic: a framework for research at the intersection of vision and language in medical AI”
Jean-Benoit Delbrouck, Khaled Saab, Maya Varma, Sabri Eyuboglu, Pierre Chambon, Jared Dunnmon, Juan Zambrano, Akshay Chaudhari and Curtis Langlotz · 2022
Later among the works it cites.
“Toward expanding the scope of radiology report summarization to multiple anatomies and modalities”
Jean-Benoit Delbrouck, Maya Varma and Curtis Langlotz · 2022
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“PrecisionFDA Truth Challenge V2: Calling variants from short and long reads in difficult-to-map regions”
Nathan. Olson, Justin Wagner, Jennifer McDaniel, Sarah. Stephens, Samuel. Westreich and et al · 2022
Later among the works it cites.
“Breast cancer diagnosis in two-view mammography using end-to-end trained efficientnet-based convolutional network”
Daniel Petrini, Carlos Shimizu, Rosimeire Roela, Gabriel Valente, Maria Folgueira and Hae Kim · 2022
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“Deeply Supervised Skin Lesions Diagnosis with Stage and Branch Attention”
Wei Dai, Rui Liu, Tianyi Wu, Min Wang, Jianqin Yin and Jun Liu · 2022
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“Exploring Advances in Transformers and CNN for Skin Lesion Diagnosis on Small Datasets”
Leandro de Lima and Renato Krohling · 2022
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“Improving radiology report generation systems by removing hallucinated references to non-existent priors”
Vignav Ramesh, Nathan Chi and Pranav Rajpurkar · 2022
Later among the works it cites.
“Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging”
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“PaLM-E: An Embodied Multimodal Language Model”
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“Multimodal image-text matching improves retrieval-based chest X-ray report generation”
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“Scaling vision transformers to 22 billion parameters”
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“Audiolm: a language modeling approach to audio generation”
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“Musiclm: Generating music from text”
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“Foundation models for generalist medical artificial intelligence”
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“Transfer learning enables predictions in network biology”
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“LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day”
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