Manuel Wiesenfarth

TL;DR: U-Net trained with T2-weighted and diffusion MRI achieves similar performance to clinical Prostate Imaging Reporting and Data System assessment in the task of detection and segmentation of lesions suspicious for sPC.

TL;DR: Both RM benefits exceeded those of ERSPC-RCs and PI-RADS in the decision regarding which patient to receive biopsy and enabled the highest reduction rate of unnecessary biopsies.

TL;DR: In this article, the authors proposed a self-supervised learning approach for pre-training of CNNs for medical instrument segmentation using unlabeled video data from the target domain.

TL;DR: To assess radiomics as a tool to determine how well lesions found suspicious on breast cancer screening X‐ray mammography can be categorized into malignant and benign with unenhanced magnetic resonance (MR) mammography with diffusion‐weighted imaging and T2‐weighting sequences is assessed.

TL;DR: The Medical Segmentation Decathlon (MSD) as mentioned in this paper was organized to evaluate the performance of image segmentation algorithms given a specific clinical problem and a set of unseen clinical problems, such as unbalanced labels, multi-site data and small objects.