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Foundation models in computational pathology promise to unlock the development of new clinical decision support systems and models for precision medicine.
“The E-cadherin/catenin complex: an important gatekeeper in breast cancer tumorigenesis and malignant progression”
Geert Berx and Frans Roy · 2001
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“Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets (MSK-IMPACT): A Hybridization Capture-Based Next-Generation Sequencing Clinical Assay for Solid Tumor Molecular Oncology”
Diana Cheng et al · 2014
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Jimmy Ba, Jamie Kiros and Geoffrey Hinton · 2016
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“OncoKB: a precision oncology knowledge base”
Debyani Chakravarty et al · 2017
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“Molecular Testing Guideline for the Selection of Patients With Lung Cancer for Treatment With Targeted Tyrosine Kinase Inhibitors: American Society of Clinical Oncology Endorsement of the College of American Pathologists/International Association for the Study of Lung Cancer/Association for Molecular Pathology Clinical Practice Guideline Update”
Gregory. Kalemkerian et al · 2017
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“Attention is all you need”
Ashish Vaswani et al · 2017
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“Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning”
Nicolas Coudray et al · 2018
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“Attention-based deep multiple instance learning”
Maximilian Ilse, Jakub Tomczak and Max Welling · 2018
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“Clinical-grade computational pathology using weakly supervised deep learning on whole slide images”
Gabriele Campanella et al · 2019
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“Language models are unsupervised multitask learners”
Alec Radford et al · 2019
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“An image is worth 16x16 words: Transformers for image recognition at scale”
Alexey Dosovitskiy et al · 2020
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“Glu variants improve transformer”
Noam Shazeer · 2020
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“Evaluating and interpreting caption prediction for histopathology images”
Renyu Zhang, Christopher Weber, Robert Grossman and Aly Khan · 2020
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“Multiple instance captioning: Learning representations from histopathology textbooks and articles”
Jevgenij Gamper and Nasir Rajpoot · 2021
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“Perceiver: General perception with iterative attention”
Andrew Jaegle et al · 2021
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“Dual-stream multiple instance learning network for whole slide image classification with self-supervised contrastive learning”
Bin Li, Yin Li and Kevin Eliceiri · 2021
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“Data-efficient and weakly supervised computational pathology on whole-slide images”
Ming Lu et al · 2021
Cited alongside, same era.
“An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy”
Sudhir Perincheri et al · 2021
Cited alongside, same era.
“Learning transferable visual models from natural language supervision”
Alec Radford et al · 2021
Cited alongside, same era.
“Transmil: Transformer based correlated multiple instance learning for whole slide image classification”
Zhuchen Shao et al · 2021
Cited alongside, same era.
“Independent real-world application of a clinical-grade automated prostate cancer detection system”
Leonard da Silva et al · 2021
Cited alongside, same era.
“Scaling Self-Supervised Learning for Histopathology with Masked Image Modeling”
Alexandre Filiot et al · 2023
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“A visual–language foundation model for pathology image analysis using medical twitter”
Zhi Huang et al · 2023
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“Benchmarking self-supervised learning on diverse pathology datasets”
Mingu Kang et al · 2023
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“Aligning Large Multi-Modal Model with Robust Instruction Tuning”
Fuxiao Liu et al · 2023
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“Towards a visual-language foundation model for computational pathology”
Ming Lu et al · 2023
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Richard Chen et al · 2022
Cited alongside, same era.
“Self supervised contrastive learning for digital histopathology”
Ozan Ciga, Tony Xu and Anne Martel · 2022
Cited alongside, same era.
“BioGPT: generative pre-trained transformer for biomedical text generation and mining”
Renqian Luo et al · 2022
Cited alongside, same era.
“Inference of captions from histopathological patches”
Masayuki Tsuneki and Fahdi Kanavati · 2022
Cited alongside, same era.
“Transformer-based unsupervised contrastive learning for histopathological image classification”
Xiyue Wang et al · 2022
Cited alongside, same era.
“Coca: Contrastive captioners are image-text foundation models”
Jiahui Yu et al · 2022
Cited alongside, same era.
Josh Achiam et al · 2023
Cited alongside, same era.
“Visual language pretrained multiple instance zero-shot transfer for histopathology images”
Ming Lu et al · 2023
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“DINOv2: Learning robust visual features without supervision”
Maxime Oquab et al · 2023
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Wenkang Qin et al · 2023
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“Clinical validation of artificial intelligence–augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection”
Patricia Raciti et al · 2023
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“Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study”
S.. Wagner et al · 2023
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“When an Image is Worth 1,024 x 1,024 Words: A Case Study in Computational Pathology”
Wenhui Wang et al · 2023
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“Towards a general-purpose foundation model for computational pathology”
Richard Chen et al · 2024
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“RudolfV: A Foundation Model by Pathologists for Pathologists”
Jonas Dippel et al · 2024
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Vishaal Udandarao et al · 2024
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“Virchow: A Million-Slide Digital Pathology Foundation Model”, 2024
Eugene Vorontsov et al · 2024
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“Novel artificial intelligence system increases the detection of prostate cancer in whole slide images of core needle biopsies”
Patricia Raciti et al · 2066
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