Lu Yi

TL;DR: A deep learning model was developed to extract visual features from volumetric chest CT scans for the detection of coronavirus 2019 and differentiate it from community-acquired pneumonia and other lung conditions.

TL;DR: The proposed CNN-RNN deep learning framework was able to accurately detect ICH and its subtypes with fast speed, suggesting its potential for assisting radiologists and physicians in their clinical diagnosis workflow.

TL;DR: A novel tree-structured convolutional gated recurrent unit (ConvGRU) model is proposed to learn the anatomical structure of the coronary artery, which considers the local spatial correlations in the input data as the convolutions are used for input-to-state as well as state- to-state transitions, thus more suitable for image analysis.

TL;DR: The proposed framework is inspired by the clinical workflow (Tanner-Whitehouse) of hand bone age assessment, which focuses on the key components of the hand, and generates the final predictions as well as the associated visual supports, similar to the assessment procedure of clinicians.

TL;DR: This is the first deep-learning based centerline extraction method that guarantees single-pixel-wide centerline for a complex tree-structured object.