18 citations · 39 across the 11 of their papers we have counts for
12 papers
HadBalance: A Plug-and-Play Unified Global Geometric Prior Framework for Generalizable Biomedical Segmentation
Zhuangzhi Gao, Feixiang Zhou, He Zhao +11
Precise biomedical image segmentation is crucial for clinical diagnosis. Geometric cues (e.g., boundary, shape, and topology) can improve structural consistency, yet most are task-…
Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification
Feixiang Zhou, Jianyang Xie, Zhuangzhi Gao +10
The missing-modality problem poses a significant challenge in image-tabular multimodal learning across a wide range of multimedia applications, including product understanding, rec…
Are Spatial-Temporal Graph Convolution Networks for Human Action Recognition Over-Parameterized?
Jianyang Xie, Yitian Zhao, Yanda Meng +3
Spatial-temporal graph convolutional networks (ST-GCNs) showcase impressive performance in skeleton-based human action recognition (HAR). However, despite the development of numero…
Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis
Chengzhi Liu, Zile Huang, Zhe Chen +6
Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data…
CathAction: A Benchmark for Endovascular Intervention Understanding
Baoru Huang, Tuan Vo, Chayun Kongtongvattana +35
Real-time visual feedback from catheterization analysis is crucial for enhancing surgical safety and efficiency during endovascular interventions. However, existing datasets are of…
Automatically Segment the Left Atrium and Scars from LGE-MRIs Using a Boundary-focused nnU-Net
Yuchen Zhang, Yanda Meng, Yalin Zheng
Atrial fibrillation (AF) is the most common cardiac arrhythmia. Accurate segmentation of the left atrial (LA) and LA scars can provide valuable information to predict treatment out…
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…
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…
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-…
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…