Peter M. Full

TL;DR: How far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies is measured, to open the door to highly accurate and fully automatic analysis of cardiac CMRI.

TL;DR: In this paper, the authors present a comprehensive analysis of biomedical image analysis challenges conducted up to now and demonstrate the importance of challenges and show that the lack of quality control has critical consequences.

TL;DR: In this article, an extension of cycle-consistent GANs, named tempCycleGAN, is proposed to improve temporal consistency for endoscopic reconstructive mitral valve procedures, which shows highly realistic results with regard to replacement of the silicone appearance of the phantom valve by intraoperative tissue texture, while keeping crucial features in the scene, such as instruments, sutures and prostheses.

TL;DR: The Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) is introduced as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain and shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice.