Breast cancer detection based on histological images using fusion of diffusion model outputs
Abstract The precise detection of breast cancer in histopathological images remains a critical challenge in computational pathology, where accurate tissue segmentation significantly enhances diagnostic accuracy. This study introduces a novel approach leveraging a Conditional Denoising Diffusion Prob...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-05744-0 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849238315896668160 |
|---|---|
| author | Younes Akbari Faseela Abdullakutty Somaya Al Maadeed Ahmed Bouridane Rifat Hamoudi |
| author_facet | Younes Akbari Faseela Abdullakutty Somaya Al Maadeed Ahmed Bouridane Rifat Hamoudi |
| author_sort | Younes Akbari |
| collection | DOAJ |
| description | Abstract The precise detection of breast cancer in histopathological images remains a critical challenge in computational pathology, where accurate tissue segmentation significantly enhances diagnostic accuracy. This study introduces a novel approach leveraging a Conditional Denoising Diffusion Probabilistic Model (DDPM) to improve breast cancer detection through advanced segmentation and feature fusion. The method employs a conditional channel within the DDPM framework, first trained on a breast cancer histopathology dataset and extended to additional datasets to achieve regional-level segmentation of tumor areas and other tissue regions. These segmented regions, combined with predicted noise from the diffusion model and original images, are processed through an EfficientNet-B0 network to extract enhanced features. A transformer decoder then fuses these features to generate final detection results. Extensive experiments optimizing the network architecture and fusion strategies were conducted, and the proposed method was evaluated across four distinct datasets, achieving a peak accuracy of 92.86% on the BRACS dataset, 100% on the BreCaHAD dataset, 96.66% the ICIAR2018 dataset. This approach represents a significant advancement in computational pathology, offering a robust tool for breast cancer detection with potential applications in broader medical imaging contexts. |
| format | Article |
| id | doaj-art-95768c9c553847748abb1066ebc87be3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-95768c9c553847748abb1066ebc87be32025-08-20T04:01:41ZengNature PortfolioScientific Reports2045-23222025-07-0115111610.1038/s41598-025-05744-0Breast cancer detection based on histological images using fusion of diffusion model outputsYounes Akbari0Faseela Abdullakutty1Somaya Al Maadeed2Ahmed Bouridane3Rifat Hamoudi4Department of Computer Science and Engineering, Qatar UniversityDepartment of Computer Science and Engineering, Qatar UniversityDepartment of Computer Science and Engineering, Qatar UniversityCenter for Data Analytics and Cybernetics, University of SharjahBIMAI-Lab, Biomedically Informed Artificial Intelligence Laboratory, University of SharjahAbstract The precise detection of breast cancer in histopathological images remains a critical challenge in computational pathology, where accurate tissue segmentation significantly enhances diagnostic accuracy. This study introduces a novel approach leveraging a Conditional Denoising Diffusion Probabilistic Model (DDPM) to improve breast cancer detection through advanced segmentation and feature fusion. The method employs a conditional channel within the DDPM framework, first trained on a breast cancer histopathology dataset and extended to additional datasets to achieve regional-level segmentation of tumor areas and other tissue regions. These segmented regions, combined with predicted noise from the diffusion model and original images, are processed through an EfficientNet-B0 network to extract enhanced features. A transformer decoder then fuses these features to generate final detection results. Extensive experiments optimizing the network architecture and fusion strategies were conducted, and the proposed method was evaluated across four distinct datasets, achieving a peak accuracy of 92.86% on the BRACS dataset, 100% on the BreCaHAD dataset, 96.66% the ICIAR2018 dataset. This approach represents a significant advancement in computational pathology, offering a robust tool for breast cancer detection with potential applications in broader medical imaging contexts.https://doi.org/10.1038/s41598-025-05744-0 |
| spellingShingle | Younes Akbari Faseela Abdullakutty Somaya Al Maadeed Ahmed Bouridane Rifat Hamoudi Breast cancer detection based on histological images using fusion of diffusion model outputs Scientific Reports |
| title | Breast cancer detection based on histological images using fusion of diffusion model outputs |
| title_full | Breast cancer detection based on histological images using fusion of diffusion model outputs |
| title_fullStr | Breast cancer detection based on histological images using fusion of diffusion model outputs |
| title_full_unstemmed | Breast cancer detection based on histological images using fusion of diffusion model outputs |
| title_short | Breast cancer detection based on histological images using fusion of diffusion model outputs |
| title_sort | breast cancer detection based on histological images using fusion of diffusion model outputs |
| url | https://doi.org/10.1038/s41598-025-05744-0 |
| work_keys_str_mv | AT younesakbari breastcancerdetectionbasedonhistologicalimagesusingfusionofdiffusionmodeloutputs AT faseelaabdullakutty breastcancerdetectionbasedonhistologicalimagesusingfusionofdiffusionmodeloutputs AT somayaalmaadeed breastcancerdetectionbasedonhistologicalimagesusingfusionofdiffusionmodeloutputs AT ahmedbouridane breastcancerdetectionbasedonhistologicalimagesusingfusionofdiffusionmodeloutputs AT rifathamoudi breastcancerdetectionbasedonhistologicalimagesusingfusionofdiffusionmodeloutputs |