Deep learning (DL) has made extensive progress in many exploration regions. Computer
vision is one of the most trending fields advancing due to extensive research in developing …

Magnetic resonance imaging (MRI) is a standard tool for the diagnosis of stroke, but its
manual interpretation by experts is arduous and time-consuming. Thus, there is a need for …

Objective Radiomic and deep learning studies based on magnetic resonance imaging (MRI)
of liver tumor are gradually increasing. Manual segmentation of normal hepatic tissue and …

We introduce InExtremIS, a weakly supervised 3D approach to train a deep image
segmentation network using particularly weak train-time annotations: only 6 extreme clicks …

Lesion segmentation in medical images is difficult yet crucial for proper diagnosis and
treatment. Identifying lesions in medical images is costly and time-consuming and requires …

Objective To develop a deep‐learning‐based multi‐task (DMT) model for joint tumor
enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular …

After completing a spaceflight, astronauts display a salient upward shift in the position of the
brain within the skull, accompanied by a redistribution of cerebrospinal fluid. Magnetic …

Simple Summary Machine learning in radiology of the central nervous system has seen
many interesting publications in the past few years. Since the focus has largely been on …

Introduction Comprehensive imaging using ultrasound and MRI of placenta accreta
spectrum (PAS) aims to prevent catastrophic haemorrhage and maternal death. Standard …

The purpose of the article is to provide a practical guide for manual and semi-automated
image segmentation of common neurosurgical cranial lesions, namely meningioma …