Multi-label text classification is a challenging task because it requires capturing label
dependencies. It becomes even more challenging when class distribution is long-tailed …

Multi-label classification algorithms based on supervised learning use all the labeled data to
train classifiers. However, in real life, many of the data are unlabeled, and it is costly to label …

Background: The purpose of this study was to conduct a systematic review for understanding
the availability and limitations of artificial intelligence (AI) approaches that could …

Objective To establish a deep learning model for the detection of hypoxic–ischemic
encephalopathy (HIE) features on CT scans and to compare various networks to determine …

Automatic pollen detection based on light microscope (LM) images is helpful for pollinosis
symptoms prevention. Recently, many deep learning methods have been proposed to …

Detecting Traumatic Brain Injuries (TBI) through imaging remains challenging due to limited
sensitivity in current methods. This study addresses the gap by proposing a novel approach …

The robustness of medical image interpretation deep learning models to common image
perturbations is crucial, as the medical images in clinical applications may be from different …

During the last years, the detection of different causes of death based on postmortem
imaging findings became more and more relevant. Especially postmortem computed …

Brain tumors are one of the deadliest diseases, with numerous implications on human
health. A brain tumor is an abnormal cell mass or growth in or around the brain. They are not …

Methods: Here, we compared whole-brain activation and changes in PTSD symptoms
between PTSD participants (n= 28) that trained to downregulate activity within either the …