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20182022
most cited3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis

23 citations · 35 across the 8 of their papers we have counts for

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Showing 2021Show all

10 papers · 1 filter

eess.IV2021★ 23 cited

3D Medical Point Transformer: Introducing Convolution to Attention Networks for Medical Point Cloud Analysis

Jianhui Yu, Chaoyi Zhang, Heng Wang +5

General point clouds have been increasingly investigated for different tasks, and recently Transformer-based networks are proposed for point cloud analysis. However, there are bare…

eess.IV2021

Voxel-wise Cross-Volume Representation Learning for 3D Neuron Reconstruction

Heng Wang, Chaoyi Zhang, Jianhui Yu +4

Automatic 3D neuron reconstruction is critical for analysing the morphology and functionality of neurons in brain circuit activities. However, the performance of existing tracing a…

cs.CV2021

DSNet: A Dual-Stream Framework for Weakly-Supervised Gigapixel Pathology Image Analysis

Tiange Xiang, Yang Song, Chaoyi Zhang +6

We present a novel weakly-supervised framework for classifying whole slide images (WSIs). WSIs, due to their gigapixel resolution, are commonly processed by patch-wise classificati…

cs.CV2021

Deep Fiber Clustering: Anatomically Informed Unsupervised Deep Learning for Fast and Effective White Matter Parcellation

Yuqian Chen, Chaoyi Zhang, Yang Song +5

White matter fiber clustering (WMFC) enables parcellation of white matter tractography for applications such as disease classification and anatomical tract segmentation. However, t…

eess.IV2021★ 1 cited

BiX-NAS: Searching Efficient Bi-directional Architecture for Medical Image Segmentation

Xinyi Wang, Tiange Xiang, Chaoyi Zhang +4

The recurrent mechanism has recently been introduced into U-Net in various medical image segmentation tasks. Existing studies have focused on promoting network recursion via reusin…

cs.LG2021

Partial Graph Reasoning for Neural Network Regularization

Tiange Xiang, Chaoyi Zhang, Yang Song +3

Regularizers help deep neural networks prevent feature co-adaptations. Dropout, as a commonly used regularization technique, stochastically disables neuron activations during netwo…