Sihong Chen

TL;DR: A heterogeneous 3D network called Med3D is designed to co-train multi-domain 3DSeg-8 so as to make a series of pre-trained models which can accelerate the training convergence speed of target 3D medical tasks and improve accuracy ranging from 3% to 20%.

TL;DR: The experimental results suggest that the predicted semantic scores from the three MTL schemes are closer to the radiologists’ ratings than the scores from single-task LASSO and elastic net regression methods, which may provide richer quantitative assessments of nodules for better support of diagnostic decision and management.

TL;DR: The Medical Segmentation Decathlon (MSD) as mentioned in this paper was organized to evaluate the generalizability of image segmentation algorithms across multiple tasks and modalities, and the MSD winner continued generalizing well to a wide range of other clinical problems for the next two years.

TL;DR: The experimental results suggest that the predicted semantic scores from MTR are closer to the radiologists’ rating than the predicted scores from single-task LASSO and elastic net regression methods.

TL;DR: A temporal affine network (TAN) is proposed to perform image analysis in a warped image space, where the shape and size variations due to the cardiac motion as well as other artifacts are largely compensated.