Luyang Luo

TL;DR: Li et al. as discussed by the authors proposed a relation-driven semi-supervised framework for medical image classification, which leverages a self-ensembling model to produce high-quality consistency targets for the unlabeled data.

TL;DR: The usefulness of 3D deep learning‐based classification of breast cancer and malignancy localization from MRI has been reported and can potentially be very useful in the clinical domain and aid radiologists in breast cancer diagnosis.

TL;DR: A deep convolutional neural network that targets representative feature learning in lithography hotspot detection and achieves comparable or better performance on the ICCAD 2012 contest benchmark compared to state-of-the-art hotspot detectors based on deep or representative machine leaning.

TL;DR: The heatmaps ofglaucomatous optic neuropathy showed that the learned features by the 3D deep-learning system used for detection of glaucoma screening were similar to those used by clinicians.

TL;DR: This paper argues that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges, and formulate the multi-label thoracic disease classification problem as weighted independent binary tasks according to the categories, enabling superior knowledge mining ability.