506 citations · 510 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…
Multiple Instance Learning with Mixed Supervision in Gleason Grading
Hao Bian, Zhuchen Shao, Yang Chen +4
With the development of computational pathology, deep learning methods for Gleason grading through whole slide images (WSIs) have excellent prospects. Since the size of WSIs is ext…
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…