49 citations · 82 across the 10 of their papers we have counts for
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DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation
Siqi Yin, Shaolei Liu, Manning Wang
Domain adaptation addresses the challenge of model performance degradation caused by domain gaps. In the typical setup for unsupervised domain adaptation, labeled data from a sourc…
Weakly Semi-supervised Whole Slide Image Classification by Two-level Cross Consistency Supervision
Linhao Qu, Shiman Li, Xiaoyuan Luo +4
Computer-aided Whole Slide Image (WSI) classification has the potential to enhance the accuracy and efficiency of clinical pathological diagnosis. It is commonly formulated as a Mu…
SP : Superpixel-propagated pseudo-label learning for weakly semi-supervised medical image segmentation
Shiman Li, Jiayue Zhao, Shaolei Liu +3
Deep learning-based medical image segmentation helps assist diagnosis and accelerate the treatment process while the model training usually requires large-scale dense annotation da…
Local Implicit Wavelet Transformer for Arbitrary-Scale Super-Resolution
Minghong Duan, Linhao Qu, Shaolei Liu +1
Implicit neural representations have recently demonstrated promising potential in arbitrary-scale Super-Resolution (SR) of images. Most existing methods predict the pixel in the SR…
Reducing Domain Gap in Frequency and Spatial domain for Cross-modality Domain Adaptation on Medical Image Segmentation
Shaolei Liu, Siqi Yin, Linhao Qu +1
Unsupervised domain adaptation (UDA) aims to learn a model trained on source domain and performs well on unlabeled target domain. In medical image segmentation field, most existing…
Robust Point Cloud Registration Framework Based on Deep Graph Matching(TPAMI Version)
Kexue Fu, Jiazheng Luo, Xiaoyuan Luo +3
3D point cloud registration is a fundamental problem in computer vision and robotics. Recently, learning-based point cloud registration methods have made great progress. However, t…