506 citations · 558 across the 6 of their papers we have counts for
6 papers
Self-Supervised Scalable Deep Compressed Sensing
Bin Chen, Xuanyu Zhang, Shuai Liu +2
Compressed sensing (CS) is a promising tool for reducing sampling costs. Current deep neural network (NN)-based CS methods face the challenges of collecting labeled measurement-gro…
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…
Progressive Content-aware Coded Hyperspectral Compressive Imaging
Xuanyu Zhang, Bin Chen, Wenzhen Zou +4
Hyperspectral imaging plays a pivotal role in a wide range of applications, like remote sensing, medicine, and cytology. By acquiring 3D hyperspectral images (HSIs) via 2D sensors,…
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…
PUERT: Probabilistic Under-sampling and Explicable Reconstruction Network for CS-MRI
Jingfen Xie, Jian Zhang, Yongbing Zhang +1
Compressed Sensing MRI (CS-MRI) aims at reconstructing de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging, thus presenting two basic issues, i.e., wh…
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…