18 citations · 34 across the 3 of their papers we have counts for
4 papers
Weakly supervised segmentation with point annotations for histopathology images via contrast-based variational model
Hongrun Zhang, Liam Burrows, Yanda Meng +5
Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training da…
DTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification
Hongrun Zhang, Yanda Meng, Yitian Zhao +4
Multiple instance learning (MIL) has been increasingly used in the classification of histopathology whole slide images (WSIs). However, MIL approaches for this specific classificat…
BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation
Yanda Meng, Hongrun Zhang, Dongxu Gao +5
Segmentation is an essential operation of image processing. The convolution operation suffers from a limited receptive field, while global modelling is fundamental to segmentation…
Spatial Uncertainty-Aware Semi-Supervised Crowd Counting
Yanda Meng, Hongrun Zhang, Yitian Zhao +4
Semi-supervised approaches for crowd counting attract attention, as the fully supervised paradigm is expensive and laborious due to its request for a large number of images of dens…