most citedDTFD-MIL: Double-Tier Feature Distillation Multiple Instance Learning for Histopathology Whole Slide Image Classification

18 citations · 38 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CV202218 cited

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…

cs.CV20222 cited

3D Dense Face Alignment with Fused Features by Aggregating CNNs and GCNs

Yanda Meng, Xu Chen, Dongxu Gao +5

In this paper, we propose a novel multi-level aggregation network to regress the coordinates of the vertices of a 3D face from a single 2D image in an end-to-end manner. This is ac…

cs.CV20223 cited

Counting with Adaptive Auxiliary Learning

Yanda Meng, Joshua Bridge, Meng Wei +5

This paper proposes an adaptive auxiliary task learning based approach for object counting problems. Unlike existing auxiliary task learning based methods, we develop an attention-…

cs.CV202115 cited

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…

cs.CV2021

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…