MCUa: Multi-level Context and Uncertainty aware Dynamic Deep Ensemble for Breast Cancer Histology Image Classification

TL;DR

This paper introduces MCUa, a multi-level context and uncertainty-aware deep ensemble model that significantly improves breast cancer histology image classification accuracy by leveraging spatial dependencies and uncertainty quantification.

Contribution

The paper presents a novel CNN architecture that incorporates multi-level context and uncertainty awareness for dynamic ensemble learning in histology image classification.

Findings

Achieved 98.11% accuracy on breast cancer histology dataset

Outperformed state-of-the-art models in histology classification

Demonstrated effectiveness of multi-level context and uncertainty modeling

Abstract

Breast histology image classification is a crucial step in the early diagnosis of breast cancer. In breast pathological diagnosis, Convolutional Neural Networks (CNNs) have demonstrated great success using digitized histology slides. However, tissue classification is still challenging due to the high visual variability of the large-sized digitized samples and the lack of contextual information. In this paper, we propose a novel CNN, called Multi-level Context and Uncertainty aware (MCUa) dynamic deep learning ensemble model.MCUamodel consists of several multi-level context-aware models to learn the spatial dependency between image patches in a layer-wise fashion. It exploits the high sensitivity to the multi-level contextual information using an uncertainty quantification component to accomplish a novel dynamic ensemble model.MCUamodelhas achieved a high accuracy of 98.11% on a breast cancer histology image dataset. Experimental results show the superior effectiveness of the proposed solution compared to the state-of-the-art histology classification models.