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 …

Despite the considerable progress in automatic abdominal multi-organ segmentation from
CT/MRI scans in recent years, a comprehensive evaluation of the models' capabilities is …

In this paper, we study the context aggregation problem in semantic segmentation.
Motivated by that the label of a pixel is the category of the object that the pixel belongs to, we …

Recent works have made great progress in semantic segmentation by exploiting richer
context, most of which are designed from a spatial perspective. In contrast to previous works …

Class imbalance has emerged as one of the major challenges for medical image
segmentation. The model cascade (MC) strategy, a popular scheme, significantly alleviates …

Abstract 3D convolution neural networks (CNN) have been proved very successful in
parsing organs or tumours in 3D medical images, but it remains sophisticated and time …

Transformer-based models, capable of learning better global dependencies, have recently
demonstrated exceptional representation learning capabilities in computer vision and …

In the current era of machine learning and radiomics, one of the challenges is the automatic
segmentation of organs and tumors. Tumor detection is mostly based on a radiologist's …

Automated and accurate 3D medical image segmentation plays an essential role in
assisting medical professionals to evaluate disease progresses and make fast therapeutic …

Prostate cancer treatment planning is largely dependent upon examination of core-needle
biopsies. The microscopic architecture of the prostate glands forms the basis for prognostic …