Purpose Artificial intelligence (AI) is playing an ever-increasing role in Neuroradiology. Methods
When designing AI-based research in neuroradiology and appreciating the literature, it …

Multi-task learning (MTL) optimizes several learning tasks simultaneously and leverages their
shared information to improve generalization and the prediction of the model for each task. …

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative
features from radiological images. Radiomic features have been shown to provide prognostic …

This paper presents a quantitative radiomics feature model for performing prostate cancer
detection using multiparametric MRI (mpMRI). It incorporates a novel tumor candidate …

… where F m is the best feature subset that we would like to find, c is the target class, f is a
feature and MI is the mutual information function. D and R are the relevance and redundancy of …

Prostate cancer is one of the most common forms of cancer and the third leading cause of
cancer death in North America. As an integrated part of computer-aided detection (CAD) tools, …

… The discriminator loss captures image characteristics using the output of an intermediate
layer of the discriminator, f ( . ) , making the discriminator act as an image encoder. (2) L R ( z i ) …

We sought to quantify contribution of radiomics and SUVmax at PET/CT to predict clinical
outcome in lung cancer patients treated with stereotactic body radiotherapy (SBRT). 150 …

Background To assess whether CT-derived texture features predict survival in patients
undergoing resection for pancreatic ductal adenocarcinoma (PDAC). Methods Thirty patients with …

Objective To differentiate combined hepatocellular cholangiocarcinoma (cHCC-CC) from
cholangiocarcinoma (CC) and hepatocellular carcinoma (HCC) using machine learning on …