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Learning Deep Nets for Gravitational Dynamics with Unknown Disturbance through Physical Knowledge Distillation: Initial Feasibility Study
Hongbin Lin, Qian Gao, Xiangyu Chu +4
Learning high-performance deep neural networks for dynamic modeling of high Degree-Of-Freedom (DOF) robots remains challenging due to the sampling complexity. Typical unknown syste…
Where is VALDO? VAscular Lesions Detection and segmentatiOn challenge at MICCAI 2021
Carole H. Sudre, Kimberlin Van Wijnen, Florian Dubost +46
Imaging markers of cerebral small vessel disease provide valuable information on brain health, but their manual assessment is time-consuming and hampered by substantial intra- and…
AutoLaparo: A New Dataset of Integrated Multi-tasks for Image-guided Surgical Automation in Laparoscopic Hysterectomy
Ziyi Wang, Bo Lu, Yonghao Long +4
Computer-assisted minimally invasive surgery has great potential in benefiting modern operating theatres. The video data streamed from the endoscope provides rich information to su…
Pseudo-label Guided Cross-video Pixel Contrast for Robotic Surgical Scene Segmentation with Limited Annotations
Yang Yu, Zixu Zhao, Yueming Jin +3
Surgical scene segmentation is fundamentally crucial for prompting cognitive assistance in robotic surgery. However, pixel-wise annotating surgical video in a frame-by-frame manner…
Test-time Adaptation with Calibration of Medical Image Classification Nets for Label Distribution Shift
Wenao Ma, Cheng Chen, Shuang Zheng +3
Class distribution plays an important role in learning deep classifiers. When the proportion of each class in the test set differs from the training set, the performance of classif…
Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary
Quande Liu, Cheng Chen, Qi Dou +1
Domain generalization typically requires data from multiple source domains for model learning. However, such strong assumption may not always hold in practice, especially in medica…