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LUMix: Improving Mixup by Better Modelling Label Uncertainty
Shuyang Sun, Jie-Neng Chen, Ruifei He +3
Modern deep networks can be better generalized when trained with noisy samples and regularization techniques. Mixup and CutMix have been proven to be effective for data augmentatio…
Sequential Learning on Liver Tumor Boundary Semantics and Prognostic Biomarker Mining
Jieneng Chen, Ke Yan, Yu-Dong Zhang +9
The boundary of tumors (hepatocellular carcinoma, or HCC) contains rich semantics: capsular invasion, visibility, smoothness, folding and protuberance, etc. Capsular invasion on tu…
TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation
Jieneng Chen, Yongyi Lu, Qihang Yu +6
Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On various medical image segment…
Efficient Semi-Supervised Gross Target Volume of Nasopharyngeal Carcinoma Segmentation via Uncertainty Rectified Pyramid Consistency
Xiangde Luo, Wenjun Liao, Jieneng Chen +6
Gross Target Volume (GTV) segmentation plays an irreplaceable role in radiotherapy planning for Nasopharyngeal Carcinoma (NPC). Despite that Convolutional Neural Networks (CNN) hav…
Deep Distance Transform for Tubular Structure Segmentation in CT Scans
Yan Wang, Xu Wei, Fengze Liu +5
Tubular structure segmentation in medical images, e.g., segmenting vessels in CT scans, serves as a vital step in the use of computers to aid in screening early stages of related d…