Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis using Auto-Augmentation and Search Optimisation Techniques

TL;DR

This paper presents a novel methodology combining auto-augmentation and search optimization to improve transformer-based breast cancer segmentation models, resulting in higher accuracy and robustness on histology slide data.

Contribution

It introduces an automated augmentation selection method using search optimization to enhance segmentation performance and robustness in breast cancer histology analysis.

Findings

Achieved a Dice Score of 84.08 for tumour segmentation

Improved model robustness to histology slide variations

Enhanced segmentation performance over state-of-the-art models

Abstract

Breast cancer remains a critical global health challenge, necessitating early and accurate detection for effective treatment. This paper introduces a methodology that combines automated image augmentation selection (RandAugment) with search optimisation strategies (Tree-based Parzen Estimator) to identify optimal values for the number of image augmentations and the magnitude of their associated augmentation parameters, leading to enhanced segmentation performance. We empirically validate our approach on breast cancer histology slides, focusing on the segmentation of cancer cells. A comparative analysis of state-of-the-art transformer-based segmentation models is conducted, including SegFormer, PoolFormer, and MaskFormer models, to establish a comprehensive baseline, before applying the augmentation methodology. Our results show that the proposed methodology leads to segmentation models that are more resilient to variations in histology slides whilst maintaining high levels of segmentation performance, and show improved segmentation of the tumour class when compared to previous research. Our best result after applying the augmentations is a Dice Score of 84.08 and an IoU score of 72.54 when segmenting the tumour class. The primary contribution of this paper is the development of a methodology that enhances segmentation performance while ensuring model robustness to data variances. This has significant implications for medical practitioners, enabling the development of more effective machine learning models for clinical applications to identify breast cancer cells from histology slides. Furthermore, the codebase accompanying this research will be released upon publication. This will facilitate further research and application development based on our methodology, thereby amplifying its impact.