Lei Cui

TL;DR: A novel pathology whole-slide diagnosis method, powered by artificial intelligence, to address the lack of interpretable diagnosis, which provides an innovative and reliable means for making diagnostic suggestions and can be deployed at low cost as next-generation, artificial intelligence-enhanced CAD technology for use in diagnostic pathology.

TL;DR: This work systemically analyzes the effect of synthetic data on segmentation and proposes a generic cross‐modality synthesis approach with an end‐to‐end 2D/3D convolutional neural network (CNN) composed of mutually‐beneficial generators and segmentors for image synthesis and segmentation tasks.

TL;DR: A novel pairwise based deep ranking hashing framework is proposed that utilizes two identical continuous matrices generated by the hyperbolic tangent (tanh) function to approximate the pairwise matrix.

TL;DR: A hybrid deep network framework is presented, aiming to efficiently integrate and exploit information from multi-view data for breast mass classification, and learns the attention-driven features of CNN as well as the semantic label dependency among different views.

TL;DR: A novel loss based attention mechanism is proposed, which simultaneously learns instance weights and predictions, and bag predictions for deep multiple instance learning, and can achieve superior bag and image classification performance over other state-of-the-art MIL methods, with obtaining higher instance precision and recall than previous attention mechanisms.