BioMedInformatics, Vol. 4, Pages 921-945: Deep Segmentation Techniques for Breast Cancer Diagnosis

BioMedInformatics, Vol. 4, Pages 921-945: Deep Segmentation Techniques for Breast Cancer Diagnosis

BioMedInformatics doi: 10.3390/biomedinformatics4020052

Authors: Storm Schutte Jia Uddin

Background: This research goes into in deep learning technologies within the realm of medical imaging, with a specific focus on the detection of anomalies in medical pathology, emphasizing breast cancer. It underscores the critical importance of segmentation techniques in identifying diseases and addresses the challenges of scarce labelled data in Whole Slide Images. Additionally, the paper provides a review, cataloguing 61 deep learning architectures identified during the study. Objectives: The aim of this study is to present and assess a novel quantitative approach utilizing specific deep learning architectures, namely the Feature Pyramid Net-work and the Linknet model, both of which integrate a ResNet34 layer encoder to enhance performance. The paper also seeks to examine the efficiency of a semi-supervised training regimen using a dual model architecture, consisting of ‘Teacher’ and ‘Student’ models, in addressing the issue of limited labelled datasets. Methods: Employing a semi-supervised training methodology, this research enables the ‘Student’ model to learn from the ‘Teacher’ model’s outputs. The study methodically evaluates the models’ stability, accuracy, and segmentation capabilities, employing metrics such as the Dice Coefficient and the Jaccard Index for comprehensive assessment. Results: The investigation reveals that the Linknet model exhibits good performance, achieving an accuracy rate of 94% in the detection of breast cancer tissues utilizing a 21-seed parameter for the initialization of model weights. It further excels in generating annotations for the ‘Student’ model, which then achieves a 91% accuracy with minimal computational demands. Conversely, the Feature Pyramid Network model demonstrates a slightly lower accuracy of 93% in the Teacher model but exhibits improved and more consistent results in the ‘Student’ model, reaching 95% accuracy with a 42-seed parameter. Conclusions: This study underscores the efficacy and potential of the Feature Pyra-mid Network and Linknet models in the domain of medical image analysis, particularly in the detection of breast cancer, and suggests their broader applicability in various medical segmentation tasks related to other pathology disorders. Furthermore, the research enhances the understanding of the pivotal role that deep learning technologies play in advancing diagnostic methods within the field of medical imaging.