Centroid-aware feature recalibration for cancer grading in pathology images

TL;DR

This paper introduces a centroid-aware feature recalibration network that improves the accuracy and robustness of cancer grading in pathology images by leveraging attention mechanisms and embedding vectors.

Contribution

The novel network uses centroid embeddings and attention to enhance cancer grading accuracy and robustness across diverse datasets.

Findings

Achieves high accuracy in colorectal cancer grading.

Maintains performance across different environmental datasets.

Enhances robustness of neural network-based pathology analysis.

Abstract

Cancer grading is an essential task in pathology. The recent developments of artificial neural networks in computational pathology have shown that these methods hold great potential for improving the accuracy and quality of cancer diagnosis. However, the issues with the robustness and reliability of such methods have not been fully resolved yet. Herein, we propose a centroid-aware feature recalibration network that can conduct cancer grading in an accurate and robust manner. The proposed network maps an input pathology image into an embedding space and adjusts it by using centroids embedding vectors of different cancer grades via attention mechanism. Equipped with the recalibrated embedding vector, the proposed network classifiers the input pathology image into a pertinent class label, i.e., cancer grade. We evaluate the proposed network using colorectal cancer datasets that were collected under different environments. The experimental results confirm that the proposed network is able to conduct cancer grading in pathology images with high accuracy regardless of the environmental changes in the datasets.