In oncology, the patient state is characterized by a whole spectrum of modalities, ranging
from radiology, histology, and genomics to electronic health records. Current artificial …

The rapid advancements in machine learning, graphics processing technologies and the
availability of medical imaging data have led to a rapid increase in the use of deep learning …

Semantic segmentation of brain tumors is a fundamental medical image analysis task
involving multiple MRI imaging modalities that can assist clinicians in diagnosing the patient …

With the fast development of various positioning techniques such as Global Position System
(GPS), mobile devices and remote sensing, spatio-temporal data has become increasingly …

Being a second most cause of mortality worldwide, cancer has been identified as a perilous
disease for human beings, where advance stage diagnosis may not help much in …

Cancer is a fatal illness often caused by genetic disorder aggregation and a variety of
pathological changes. Cancerous cells are abnormal areas often growing in any part of …

This paper reviews state-of-the-art research solutions across the spectrum of medical
imaging informatics, discusses clinical translation, and provides future directions for …

The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …

Recent advances and future perspectives of machine learning techniques offer promising
applications in medical imaging. Machine learning has the potential to improve different …

Purpose To evaluate the trustworthiness of saliency maps for abnormality localization in
medical imaging. Materials and Methods Using two large publicly available radiology …