Andrew Y. Ng

TL;DR: A deep learning model for detecting general abnormalities and specific diagnoses (anterior cruciate ligament [ACL] tears and meniscal tears) on knee MRI exams is developed and the assertion that deep learning models can improve the performance of clinical experts during medical imaging interpretation is supported.

TL;DR: In this paper, the authors propose a new approach to the problem of policy search for a Markov decision process (MDP) or a partially observable MDP (POMDP) given a model.

TL;DR: This paper applies large-scale algorithms for learning the features automatically from unlabeled data to construct highly effective classifiers for both detection and recognition to be used in a high accuracy end-to-end system.

TL;DR: This work proposes a new approach to the problem of searching a space of policies for a Markov decision process (MDP) or a partially observable Markov decisions process (POMDP), given a model, based on the following observation: Any (PO)MDP can be transformed into an "equivalent" POMDP in which all state transitions are deterministic.

TL;DR: The state-of-the-art results show that the learned breast density scores have a very strong positive relationship with manual ones, and that the learning texture scores are predictive of breast cancer.