DIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images

TL;DR

This paper introduces DIOR-ViT, a transformer-based model that incorporates differential ordinal learning to improve cancer grading accuracy by leveraging the inherent order among cancer grades in pathology images.

Contribution

It proposes a novel differential ordinal learning framework integrated with a transformer model for cancer grading, enhancing traditional categorical classification methods.

Findings

Outperforms conventional cancer grading approaches

Improves accuracy and reliability of cancer classification

Applicable to other diseases with ordinal labels

Abstract

In computational pathology, cancer grading has been mainly studied as a categorical classification problem, which does not utilize the ordering nature of cancer grades such as the higher the grade is, the worse the cancer is. To incorporate the ordering relationship among cancer grades, we introduce a differential ordinal learning problem in which we define and learn the degree of difference in the categorical class labels between pairs of samples by using their differences in the feature space. To this end, we propose a transformer-based neural network that simultaneously conducts both categorical classification and differential ordinal classification for cancer grading. We also propose a tailored loss function for differential ordinal learning. Evaluating the proposed method on three different types of cancer datasets, we demonstrate that the adoption of differential ordinal learning can improve the accuracy and reliability of cancer grading, outperforming conventional cancer grading approaches. The proposed approach should be applicable to other diseases and problems as they involve ordinal relationship among class labels.