Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

Deep learning (DL) algorithms have rapidly become a robust tool for analyzing medical
images. They have been used extensively for medical image segmentation as the first and …

With the unprecedented developments in deep learning, automatic segmentation of main
abdominal organs seems to be a solved problem as state-of-the-art (SOTA) methods have …

Background: Over half a million individuals are diagnosed with head and neck cancer each
year globally. Radiotherapy is an important curative treatment for this disease, but it requires …

Whole abdominal organ segmentation is important in diagnosing abdomen lesions,
radiotherapy, and follow-up. However, oncologists' delineating all abdominal organs from …

Chest computed tomography (CT) imaging has become indispensable for staging and
managing coronavirus disease 2019 (COVID-19), and current evaluation of anomalies …

Deep learning empowers the mainstream medical image segmentation methods.
Nevertheless, current deep segmentation approaches are not capable of efficiently and …

Neural architecture search (NAS) has attracted increasing attention. In recent years,
individual search methods have been replaced by weight-sharing search methods for higher …

Accurate organ-at-risk (OAR) segmentation is critical to reduce radiotherapy complications.
Consensus guidelines recommend delineating over 40 OARs in the head-and-neck (H&N) …

The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …