Skip to main content
Article

Enhancing mitotic figure detection using attention modules in digital pathology

Author: May Hlaing Kyi (University of West London)

  • Enhancing mitotic figure detection using attention modules in digital pathology

    Article

    Enhancing mitotic figure detection using attention modules in digital pathology

    Author:

Abstract

Presented at the UWL Annual Doctoral Students' Conference, Friday 12 July 2024.

Keywords: mitotic figures, cancer prognosis

How to Cite:

Hlaing Kyi, M., (2025) “Enhancing mitotic figure detection using attention modules in digital pathology”, New Vistas 11(1). doi: https://doi.org/10.36828/newvistas.275

Downloads:
Download HTML

54 Views

8 Downloads

Published on
2025-02-19

Peer Reviewed

2cd1a3f7-dd75-4165-bc22-3f0fc904ebaa

Enhancing mitotic figure detection using attention

modules in digital pathology

May Hlaing Kyi

School of Computing and Engineering

Supervisors:

Dr Neda Azarmehr

School of Computing and Engineering

Professor Massoud Zolgharni

School of Computing and Engineering

Counting mitotic figures is essential for cancer grading and prognosis; however, manual counting is tedious and prone to pathologist discordance due to diverse appearances of mitotic figures. Developing an effective detection model is difficult due to the complex growth patterns and similarities to non-mitotic cells. Convolutional neural networks have been used to automate this process, yet current results are still inadequate for clinical use.

Aim and objective:

This study aims to investigate the potential of incorporating various state-of-the-art attention mechanisms into a ResNet backbone for differentiating mitotic figures from mimics in Whole Slide Images (WSIs). The objectives are to develop and evaluate the developed framework for the automated mitotic figure detection and to visualise detected mitotic figures at the WSI level.

Research questions:

How effectively do attention modules enhance the accuracy of mitotic figure detection in WSI?

Research design and method:

This study employs a detection and classification task (mitosis versus mimics) based on the RetinaNet model. We designed and trained the RetinaNet models with several state-of-the-art attention modules including the Convolutional Block Attention Module (CBAM), Squeeze-and-Excitation (SE), Enhanced Channel Attention, and Hybrid Attention Module integrated into ResNet50 backbone. All developed models were evaluated using the public Canine Mammary Carcinoma dataset. The performance of models is evaluated using several metrics, including F1-score, precision, and recall, across test-set.

Findings:

Results indicate that the models with a ResNet50 backbone incorporating CBAM and SE attention mechanisms achieved F1 scores of 0.793 and 0.784, respectively, outperforming the standard RetinaNet architecture.

Implications of the findings:

Utilising attention mechanism can improve the accuracy of mitosis detection model. This improvement leads to more accurate diagnoses, reduced variability, and ultimately personalised treatment plans.