VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images.
Journal:
Computer methods and programs in biomedicine
Published Date:
Apr 21, 2024
Abstract
BACKGROUND AND OBJECTIVE: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis.