Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging

Digital pathology evaluation in the multicenter nephrotic syndrome study network (neptune)

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Language models are few-shot learners

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On the opportunities and risks of foundation models

Original

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Ai applications in renal pathology

Yuankai Huo, Ruining Deng, Quan Liu, Agnes B Fogo, and Haichun Yang · 2021

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Development and evaluation of deep learning–based segmentation of histologic structures in the kidney cortex with multiple histologic stains

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Scaling up visual and vision-language representation learning with noisy text supervision

Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021

Cited alongside, same era.

Beds: Bagging ensemble deep segmentation for nucleus segmentation with testing stage stain augmentation

Xing Li, Haichun Yang, Jiaxin He, Aadarsh Jha, Agnes B Fogo, Lee E Wheless, Shilin Zhao, and Yuankai Huo · 2021

Cited alongside, same era.

Simtriplet: Simple triplet representation learning with a single gpu

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Learning transferable visual models from natural language supervision

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Zero-shot text-to-image generation

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Omni-seg: A scale-aware dynamic network for renal pathological image segmentation

Ruining Deng, Quan Liu, Can Cui, Tianyuan Yao, Jun Long, Zuhayr Asad, R Michael Womick, Zheyu Zhu, Agnes B Fogo, Shilin Zhao, et al · 2023

Closest in time.

Segment anything

Original

Alexander Kirillov, Eric Mintun, Nikhila Ravi, Hanzi Mao, Chloe Rolland, Laura Gustafson, Tete Xiao, Spencer Whitehead, Alexander C Berg, Wan-Yen Lo, et al · 2023

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Gpt-4 technical report, 2023

OpenAI · 2023

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