Citation

Hasan, Md Jahid and Ahmad, Wan Siti Halimatul Munirah Wan and Ahmad Fauzi, Mohammad Faizal and Lee, Jenny Tung Hiong and Khor, See Yee and Looi, Lai Meng and Abas, Fazly Salleh and Adam, Afzan and Chan, Elaine Wan Ling (2024) Real-time segmentation and classification of whole-slide images for tumor biomarker scoring. Journal of King Saud University - Computer and Information Sciences, 36 (9). p. 102204. ISSN 1319-1578

Text 1-s2.0-S1319157824002933-main.pdf - Published Version Restricted to Repository staff only Download (7MB)

Abstract

Histopathology image segmentation and classification are essential for diagnosing and treating breast cancer. This study introduced a highly accurate segmentation and classification for histopathology images using a single architecture. We utilized the famous segmentation architectures, SegNet and U-Net, and modified the decoder to attach ResNet, VGG and DenseNet to perform classification tasks. These hybrid models are integrated with Stardist as the backbone, and implemented in a real-time pathologist workflow with a graphical user interface. These models were trained and tested offline using the ER-IHC-stained private and H&E-stained public datasets (MoNuSeg). For real-time evaluation, the proposed model was evaluated using PR-IHC-stained glass slides. It achieved the highest segmentation pixel-based F1-score of 0.902 and 0.903 for private and public datasets respectively, and a classification-based F1-score of 0.833 for private dataset. The experiment shows the robustness of our method where a model trained on ER-IHC dataset able to perform well on real-time microscopy of PR-IHC slides on both 20x and 40x magnification. This will help the pathologists with a quick decision-making process

Item Type:	Article
Uncontrolled Keywords:	Breast cancer Deep learning Histology images
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Divisions:	Faculty of Engineering (FOE)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	04 Nov 2024 02:07
Last Modified:	04 Nov 2024 02:07
URII:	http://shdl.mmu.edu.my/id/eprint/13126

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