Sam Blackwell

TL;DR: A novel deep learning architecture performs device-independent tissue segmentation of clinical 3D retinal images followed by separate diagnostic classification that meets or exceeds human expert clinical diagnoses of retinal disease.

TL;DR: In this article, an AlphaFold model trained specifically for multimeric inputs of known stoichiometry was proposed, which significantly increases the accuracy of predicted multimimeric interfaces over input-adapted single-chain AlphaFolds.

TL;DR: This work presents the first massively distributed architecture for deep reinforcement learning, using a distributed neural network to represent the value function or behaviour policy, and a distributed store of experience to implement the Deep Q-Network algorithm.

TL;DR: A 3D U-Net architecture that achieves performance similar to experts in delineating a wide range of head and neck OARs is demonstrated that could improve the effectiveness of radiotherapy pathways.

TL;DR: In this article, a 3D U-Net architecture was used to segment head and neck organs at risk commonly segmented in clinical practice, and the model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practices and segmentations created by experienced radiographers.