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20212024
most citedTransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

506 citations · 510 across the 5 of their papers we have counts for

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5 papers

eess.IV2024

Prior-guided Diffusion Model for Cell Segmentation in Quantitative Phase Imaging

Zhuchen Shao, Mark A. Anastasio, Hua Li

Purpose: Quantitative phase imaging (QPI) is a label-free technique that provides high-contrast images of tissues and cells without the use of chemicals or dyes. Accurate semantic…

eess.IV2023★ 2 cited

Semi-Supervised Semantic Segmentation of Cell Nuclei via Diffusion-based Large-Scale Pre-Training and Collaborative Learning

Zhuchen Shao, Sourya Sengupta, Hua Li +1

Automated semantic segmentation of cell nuclei in microscopic images is crucial for disease diagnosis and tissue microenvironment analysis. Nonetheless, this task presents challeng…

cs.CV2023

HVTSurv: Hierarchical Vision Transformer for Patient-Level Survival Prediction from Whole Slide Image

Zhuchen Shao, Yang Chen, Hao Bian +3

Survival prediction based on whole slide images (WSIs) is a challenging task for patient-level multiple instance learning (MIL). Due to the vast amount of data for a patient (one o…

cs.CV2023★ 2 cited

AugDiff: Diffusion based Feature Augmentation for Multiple Instance Learning in Whole Slide Image

Zhuchen Shao, Liuxi Dai, Yifeng Wang +2

Multiple Instance Learning (MIL), a powerful strategy for weakly supervised learning, is able to perform various prediction tasks on gigapixel Whole Slide Images (WSIs). However, t…

cs.CV2021★ 506 cited

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

Zhuchen Shao, Hao Bian, Yang Chen +4

Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL met…