Yueming Jin

TL;DR: The proposed 3D DSN is capable of conducting volume‐to‐volume learning and inference, which can eliminate redundant computations and alleviate the risk of over‐fitting on limited training data, and the3D deep supervision mechanism can effectively cope with the optimization problem of gradients vanishing or exploding when training a 3D deep model.

TL;DR: A novel 3D deeply supervised network (3D DSN) is presented which takes advantage of a fully convolutional architecture which performs efficient end-to-end learning and inference and introduces a deep supervision mechanism during the learning process to combat potential optimization difficulties.

TL;DR: Wang et al. as mentioned in this paper proposed a 3D deeply supervised network (3D DSN) for liver segmentation from CT volumes, which takes advantage of a fully convolutional architecture and introduces a deep supervision mechanism during the learning process to combat potential optimization difficulties.

TL;DR: Based on the phase transition-sensitive predictions from the SV-RCNet, a simple yet effective inference scheme, namely the prior knowledge inference (PKI), by leveraging the natural characteristic of surgical video is proposed, which improves the consistency of results and largely boosts the recognition performance.

TL;DR: The AI contouring tool improved primary gross tumor contouring accuracy of nasopharyngeal carcinoma, which could have a positive impact on tumor control and patient survival.