Jianhong Cheng

TL;DR: A comprehensive literature review of U-shaped networks applied to medical image segmentation tasks, focusing on the architectures, extended mechanisms and application areas in these studies.

TL;DR: A fully automated multimodal MRI-based multi-task learning framework for simultaneous glioma segmentation and IDH genotyping and an uncertainty-aware pseudo-label selection is proposed to generate IDH pseudo-labels from larger unlabeled data for improving the accuracy of IDHgenotyping by using semi-supervised learning.

TL;DR: Wang et al. as mentioned in this paper proposed a deep neural network model termed as multimodal disentangled variational autoencoder (MMD-VAE) for glioma grading based on radiomics features extracted from preoperative multi-modal MRI images.

TL;DR: In this article , the authors used deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the ICU.

TL;DR: This study proposed a novel radiomics-based pipeline by incorporating the intratumoral and peritumoral features extracted from preoperative mpMRI scans to accurately and noninvasively predict glioma grade and demonstrated to have strong generalization performance in an external validation dataset with 65 patients.