Michael Yeung

TL;DR: It is found that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases, which is a major weakness, given the urgency with which validated COVID-19 models are needed.

TL;DR: In this paper, the authors proposed a Unified Focal loss, a new framework that generalizes Dice and cross entropy-based losses for handling class imbalance, and evaluated their proposed loss function on three highly class imbalanced, publicly available medical imaging datasets.

TL;DR: Focus U-Net as mentioned in this paper is a dual attention-gated deep neural network, which combines efficient spatial and channel-based attention into a single Focus Gate module to encourage selective learning of polyp features.

TL;DR: In this article , the authors systematically reviewed the current literature evaluating the ability of fully-automated deep learning (DL) and semi-automatic traditional machine learning (TML) MRI-based artificial intelligence (AI) methods to differentiate clinically significant prostate cancer (csPCa) from indolent PCa (iPCa), and benign conditions.

TL;DR: In this article, a Mixed Focal loss function, a new compound loss function derived from modified variants of the Focal Loss and Focal Dice loss functions, was proposed for image segmentation.