Enhancing Transformer-Based Segmentation for Breast Cancer Diagnosis
  using Auto-Augmentation and Search Optimisation Techniques
Leon Hamnett Mary Adewunmi Modinat Abayomi Kayode Raheem Fahad Ahmed
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.