Han Wang
Sichuan University
8 Papers
Han Wang is an academic researcher from Sichuan University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 1, co-authored 3 publications.
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Papers
TransUNet＋: Redesigning the skip connection to enhance features in medical image segmentation
TL;DR: TransUNet as mentioned in this paper combines convolutional neural networks (CNNs) and transformers for medical image segmentation, which can achieve promising results with a redesigned skip connection for improving global attention by using the score matrix of the transformer block.
54
DeepEC: An error correction framework for dose prediction and organ segmentation using deep neural networks
TL;DR: This paper treats organ segmentation and dose prediction as similar tasks, and proposes an error correction framework to improve their performance based on the same mechanism, and shows that the framework is superior to other state‐of‐the‐art methods in both tasks.
22
An Explainable Coarse-to-fine Survival Analysis Method on Multi-center Whole Slide Images
TL;DR: Wang et al. as discussed by the authors proposed a coarse-to-fine survival model based on graph neural networks, which not only solves the above two problems but also achieves the best survival prediction performance.
2
An attention-based neural network model for automatic partition of abdominal lymph nodes in CT imaging
TL;DR: The proposed method is expected to be introduced in future medical scenarios, which will help doctors to optimize the diagnosis workflow and improve partition sensitivity and performs better in experimental metrics than other prevalent methods.
1
TeachMe: a web-based teaching system for annotating abdominal lymph nodes
Shuaihua Chen,Hao Huang,Xuyang Yang,Han Wang,Mingtian Wei,Haixian Zhang,Ziqiang Wang,Zhang Yi +7 more
TL;DR: TeachMe as discussed by the authors is a web-based teaching system for annotating abdominal lymph nodes, which has a three-level annotation-review workflow to construct an expert database of abdominal nodes and a feedback mechanism helping junior doctors to learn the tricks of interpreting abdominal medical images.