Advances in computerized image analysis and the use of artificial intelligence–based
approaches for image-based analysis and construction of prediction algorithms represent a …

This study aims to investigate the predictive value of preoperative whole-tumor histogram
analysis of multi-parametric MRI for histological subtypes in patients with lung cancer brain …

Simple Summary An accurate survival analysis is crucial for disease management in
glioblastoma (GBM) patients. Due to the ability of the diffusion MRI techniques of providing a …

Gliomas are the most common malignant brain tumours with intrinsic heterogeneity.
Accurate segmentation of gliomas and their sub-regions on multi-parametric magnetic …

Purpose: The aim of this study was to investigate the diagnostic value of machine-learning
models with radiomic features and clinical features in preoperative differentiation of common …

The recent advancements in toxicogenomics have led to the availability of large omics data
sets, representing the starting point for studying the exposure mechanism of action and …

White matter maturation has been characterized by diffusion tensor (DT) metrics. However,
maturational processes and degrees are not fully investigated due to limitations of univariate …

Glioblastoma, an aggressive brain tumor prevalent in adults, exhibits heterogeneity in its
microstructures and vascular patterns. The delineation of its subregions could facilitate the …

Automatic brain tumor segmentation is one of the crucial problems nowadays among other
directions and domains where daily clinical workflow requires to put a lot of efforts while …

We present an expectation-maximization (EM) regularized deep learning (EMReDL)
approach for weakly supervised tumor segmentation using partially labelled MRI. The …