The density of mitotic figures (MF) within tumor tissue is known to be highly correlated with
tumor proliferation and thus is an important marker in tumor grading. Recognition of MF by …

Recognition of mitotic figures in histologic tumor specimens is highly relevant to patient
outcome assessment. This task is challenging for algorithms and human experts alike, with …

Although the existing deep supervised solutions have achieved some great successes in
medical image segmentation, they have the following shortcomings;(i) semantic difference …

Computer-aided automatic brain-tumor detection has been a very important biomedical
engineering research problem for years. As the sizes and shapes of tumors vary …

In cardiac cine magnetic resonance imaging (MRI), the heart is repeatedly imaged at
numerous time points during the cardiac cycle. Frequently, the temporal evolution of a …

Myocardial pathology segmentation is essential for the diagnosis and treatment of patients
suffering from myocardial infarction. In this work, we propose an end-to-end deep learning …

Cardiac magnetic resonance imaging (MRI) segmentation task refers to the accurate
segmentation of ventricle and myocardium, which is a prerequisite for evaluating the …

Background The quality of segmentation of thyroid nodules in ultrasound images is a crucial
factor in preventing the cancerization of thyroid nodules. However, the existing standards for …

Camouflaged object detection (COD) in monocular images has garnered broad attention
recently, aiming to segment objects that have high intrinsic similarity with their surroundings …

Nuclei segmentation is a prerequisite and an essential step in cancer detection and
prognosis. Automatic nuclei segmentation from the histopathological images is challenging …