DeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification

TL;DR

DeepHistoViT is a novel transformer-based framework that enhances interpretability and achieves state-of-the-art accuracy in classifying histopathological images for various cancers, aiding clinical diagnosis.

Contribution

It introduces a customized Vision Transformer with an attention mechanism for cellular detail capture and interpretability in histopathology classification.

Findings

Achieves 100% accuracy on lung and colon cancer datasets.

Demonstrates high performance on leukemia dataset with over 99.8% accuracy.

Provides interpretable attention maps highlighting diagnostic regions.

Abstract

Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive, and subject to inter-observer variability, creating a demand for reliable computer-assisted diagnostic tools. Recent advances in deep learning, particularly transformer-based architectures, have shown strong potential for modelling complex spatial dependencies in medical images. In this work, we propose DeepHistoViT, a transformer-based framework for automated classification of histopathological images. The model employs a customized Vision Transformer architecture with an integrated attention mechanism designed to capture fine-grained cellular structures while improving interpretability through attention-based localization of diagnostically relevant regions. The framework is evaluated on three publicly available histopathology datasets covering lung cancer, colon cancer, and acute lymphoblastic leukaemia. Experimental results demonstrate state-of-the-art performance across all datasets, with classification accuracy, precision, recall, F1-score, and ROC-AUC reaching 100 percent on the lung and colon cancer datasets, and 99.85 percent, 99.84 percent, 99.86 percent, 99.85 percent, and 99.99 percent respectively on the acute lymphoblastic leukaemia dataset. All performance metrics are reported with 95 percent confidence intervals. These results highlight the effectiveness of transformer-based architectures for histopathological image analysis and demonstrate the potential of DeepHistoViT as an interpretable computer-assisted diagnostic tool to support pathologists in clinical decision-making.