FUSC: Fetal Ultrasound Semantic Clustering of Second-Trimester Scans Using Deep Self-Supervised Learning.

Objective: The aim of this study was address the challenges posed by the manual labeling of fetal ultrasound images by introducing an unsupervised approach, the fetal ultrasound semantic clustering (FUSC) method. The primary objective was to automatically cluster a large volume of ultrasound images into various fetal views, reducing or eliminating the need for labor-intensive manual labeling.

Methods: The FUSC method was developed by using a substantial data set comprising 88,063 images.

MGMT promoter methylation status prediction using MRI scans? An extensive experimental evaluation of deep learning models.

The number of studies on deep learning for medical diagnosis is expanding, and these systems are often claimed to outperform clinicians. However, only a few systems have shown medical efficacy. From this perspective, we examine a wide range of deep learning algorithms for the assessment of glioblastoma - a common brain tumor in older adults that is lethal.