Yuanyuan Wang

TL;DR: This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks.

TL;DR: The performance of DLR for predicting the mutation status of isocitrate dehydrogenase 1 (IDH1) was validated in a dataset of 151 patients with low-grade glioma and the AUC of IDH1 estimation was improved to 95% using DLR based on multiple-modality MR images, suggesting DLR could be a powerful way to extract deep information from medical images.

TL;DR: The authors show that, in addition to ultrasound, shear wave elastography can be used to diagnose breast cancer and, in conjunction with deep learning and radiomics, can predict whether the disease has spread to axillary lymph nodes.

TL;DR: Radiomics is a potentially useful approach for estimating IDH1 mutation status noninvasively using conventional T2-FLAIR MRI images, and the estimation accuracy could potentially be improved by using multiple imaging modalities.

TL;DR: This large-scale benchmarking study makes a significant step towards much-improved segmentation methods for atrial LGE-MRIs, and will serve as an important benchmark for evaluating and comparing the future works in the field.