Mateusz Buda

TL;DR: The effect of class imbalance on classification performance is detrimental; the method of addressing class imbalance that emerged as dominant in almost all analyzed scenarios was oversampling; and thresholding should be applied to compensate for prior class probabilities when overall number of properly classified cases is of interest.

TL;DR: Deep learning is a branch of artificial intelligence where networks of simple interconnected units are used to extract patterns from data in order to solve complex problems as mentioned in this paper, and it has shown promising performance in a variety of sophisticated tasks, especially those related to images.

TL;DR: The general context of radiology and opportunities for application of deep‐learning algorithms and basic concepts of deep learning are discussed, including convolutional neural networks and a survey of the research in deep learning applied to radiology are presented.

TL;DR: In this paper, the authors proposed a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes.

TL;DR: Sensitivity and specificity of a deep learning algorithm for thyroid nodule biopsy recommendations was similar to that of expert radiologists who used American College of Radiology Thyroid Imaging and Reporting Data System guidelines.