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Separated Contrastive Learning for Organ-at-Risk and Gross-Tumor-Volume Segmentation with Limited Annotation
Jiacheng Wang, Xiaomeng Li, Yiming Han +3
Automatic delineation of organ-at-risk (OAR) and gross-tumor-volume (GTV) is of great significance for radiotherapy planning. However, it is a challenging task to learn powerful re…
Real-time landmark detection for precise endoscopic submucosal dissection via shape-aware relation network
Jiacheng Wang, Yueming Jin, Shuntian Cai +4
We propose a novel shape-aware relation network for accurate and real-time landmark detection in endoscopic submucosal dissection (ESD) surgery. This task is of great clinical sign…
Unsupervised Representation Learning Meets Pseudo-Label Supervised Self-Distillation: A New Approach to Rare Disease Classification
Jinghan Sun, Dong Wei, Kai Ma +2
Rare diseases are characterized by low prevalence and are often chronically debilitating or life-threatening. Imaging-based classification of rare diseases is challenging due to th…
Boundary-aware Transformers for Skin Lesion Segmentation
Jiacheng Wang, Lan Wei, Liansheng Wang +3
Skin lesion segmentation from dermoscopy images is of great importance for improving the quantitative analysis of skin cancer. However, the automatic segmentation of melanoma is a…
Efficient Global-Local Memory for Real-time Instrument Segmentation of Robotic Surgical Video
Jiacheng Wang, Yueming Jin, Liansheng Wang +3
Performing a real-time and accurate instrument segmentation from videos is of great significance for improving the performance of robotic-assisted surgery. We identify two importan…
nnFormer: Interleaved Transformer for Volumetric Segmentation
Hong-Yu Zhou, Jiansen Guo, Yinghao Zhang +3
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies,…
RECIST-Net: Lesion detection via grouping keypoints on RECIST-based annotation
Cong Xie, Shilei Cao, Dong Wei +6
Universal lesion detection in computed tomography (CT) images is an important yet challenging task due to the large variations in lesion type, size, shape, and appearance. Consider…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…