Image segmentation is a fundamental building block of automatic medical applications. It
has been greatly improved since the emergence of deep neural networks. However, deep …

Abstract Coronavirus disease (COVID-19) is a severe infectious disease that causes
respiratory illness and has had devastating medical and economic consequences globally …

Retinopathy of prematurity (ROP) is currently one of the leading causes of infant blindness
worldwide. Recently significant progress has been made in deep learning-based computer …

Background Semi‐supervised learning is becoming an effective solution for medical image
segmentation because of the lack of a large amount of labeled data. Purpose Consistency …

Medical image segmentation is one of the major challenges addressed by machine learning
methods. However, these methods profoundly depend on a large amount of annotated data …

Nasopharyngeal carcinoma (NPC) is primarily treated with radiation therapy. Accurate
delineation of target volumes and organs at risk is important. However, manual delineation …

Recent medical image segmentation methods heavily rely on large-scale training data and
high-quality annotations. However, these resources are hard to obtain due to the limitation of …

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging
of biological tissues at high resolution that has revolutionized retinal imaging. A major …

Biomarker inference from biomedical images is one of the main tasks of medical image
analysis. Standard techniques follow a segmentation-and-measure strategy, where the …