Non-annotated renal histopathological image analysis with deep ensemble learning.

ABSTRACT:

Background

Renal cancer is one of the leading causes of cancer-related deaths worldwide, and early detection of renal cancer can significantly improve the patients' survival rate. However, the manual analysis of renal tissue in the current clinical practices is labor-intensive, prone to inter-pathologist variations and easy to miss the important cancer markers, especially in the early stage.

Methods

In this work, we developed deep convolutional neural network (CNN) based heterogeneous ensemble models for automated analysis of renal histopathological images without detailed annotations. The proposed method would first segment the histopathological tissue into patches with different magnification factors, then classify the generated patches into normal and tumor tissues using

SUBMITTER: Koo JC 

PROVIDER: S-EPMC10498232 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature