H
Huisi Wu
Researcher at Shenzhen University
Publications - 78
Citations - 667
Huisi Wu is an academic researcher from Shenzhen University. The author has contributed to research in topics: Computer science & Segmentation. The author has an hindex of 10, co-authored 52 publications receiving 327 citations. Previous affiliations of Huisi Wu include The Chinese University of Hong Kong.
Papers
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Journal ArticleDOI
Automatic Symmetry Detection From Brain MRI Based on a 2-Channel Convolutional Neural Network
TL;DR: This article proposes an automatic symmetry detection method for brain MR images in 2-D slices based on a 2-channel convolutional neural network (CNN) that achieves excellent performance for symmetry detection and compares with the state-of-the-art methods.
Journal ArticleDOI
PolypSeg+: A Lightweight Context-Aware Network for Real-Time Polyp Segmentation
TL;DR: A novel lightweight context-aware network, namely, PolypSeg+, attempting to capture distinguishable features of polyps without increasing network complexity and sacrificing time performance, which consistently outperforms other state-of-the-art networks by achieving better segmentation accuracy in much less running time.
Journal ArticleDOI
FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation
TL;DR: This work proposes a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels, which outperforms the state-of-the-art methods by a large margin.
Journal ArticleDOI
Fast and robust symmetry detection for brain images based on parallel scale‐invariant feature transform matching and voting
TL;DR: This article presents a fast and robust symmetry detection method for automatically extracting symmetry axis (fissure line) from a brain image based on a set of scale‐invariant feature transform (SIFT) features, where the symmetry axis is determined by parallel matching and voting of distinctive features within the brain image.
Book ChapterDOI
Fast and Robust Leaf Recognition Based on Rotation Invariant Shape Context
TL;DR: A fast and robust method for leaf recognition by identifying leaves based on rotation invariant shape context (RISC) and summed squared differences (SSD) color matching and using SSD color matching to distinguish plants having the same shape context but with different colors.