Deep learning has become a thriving force in the computer aided diagnosis of liver cancer,
as it solves extremely complicated challenges with high accuracy over time and facilitates …

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant
contributor to worldwide cancer-related deaths. Various medical imaging techniques …

This paper targets on self-supervised tumor segmentation. We make the following
contributions:(i) we take inspiration from the observation that tumors are often characterised …

Breast tumor segmentation is critical to the diagnosis and treatment of breast cancer. In
clinical breast cancer analysis, experts often examine multi-modal images since such …

Objectives Machine learning (ML) for medical imaging is emerging for several organs and
image modalities. Our objectives were to provide clinicians with an overview of this field by …

Aiming at the difficult problem of coal gangue image segmentation in complex backgrounds,
this paper proposes an image segmentation method based on the edge detection theory of …

Breast tumor segmentation plays a critical role in the diagnosis and treatment of breast
diseases. Current breast tumor segmentation methods are mainly deep learning (DL) based …

Recently, interactive segmentation models have achieved remarkable success in the field of
biomedical images. However, these models rely on the accurate and high-quality interaction …

Radiologists possess diverse training and clinical experiences, leading to variations in the
segmentation annotations of lung nodules and resulting in segmentation uncertainty …

Automatic liver tumor detection from computed tomography (CT) makes clinical
examinations more accurate. However, deep learning-based detection algorithms are …