Silas Nyboe Ørting

TL;DR: This survey reviews studies applying crowdsourcing to the analysis of medical images, published prior to July 2018, and identifies common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach.

TL;DR: In this article, the authors provide guidance to researchers considering using crowdsourcing methodologies in their own medical imaging analysis, identifying common approaches, challenges and considerations, providing guidance of utility to researchers adopting this approach.

TL;DR: The volumetric image classification problem is posed as a multi-instance classification problem and a novel method is proposed to adaptively select positive instances from positive bags during the training phase, which produces classifiers that have similar performance to fully supervised methods.

TL;DR: An end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue such that its proportions fit the ground truth intervals to outperform traditional lung densitometry and two recently published methods for empysema quantification by a large margin.

TL;DR: A machine learning method is trained to predict emphysema from visually assessed expert labels using a multiple instance learning approach to predict both scan-level and region-level emphySEma presence.