Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model
Abstract Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive a...
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BMC
2025-01-01
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| Series: | BMC Medical Imaging |
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| Online Access: | https://doi.org/10.1186/s12880-024-01522-y |
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| author | Afaf Saad Noha Ghatwary Safa M. Gasser Mohamed S. ElMahallawy |
| author_facet | Afaf Saad Noha Ghatwary Safa M. Gasser Mohamed S. ElMahallawy |
| author_sort | Afaf Saad |
| collection | DOAJ |
| description | Abstract Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder’s performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care. |
| format | Article |
| id | doaj-art-9b78858126194f93a69cb5e61ca20480 |
| institution | Kabale University |
| issn | 1471-2342 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj-art-9b78858126194f93a69cb5e61ca204802025-01-12T12:44:47ZengBMCBMC Medical Imaging1471-23422025-01-0125111510.1186/s12880-024-01522-yAutomatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN modelAfaf Saad0Noha Ghatwary1Safa M. Gasser2Mohamed S. ElMahallawy3Electronics and Communications, Arab Academy for ScienceDepartment of Computer Engineering, Arab Academy for ScienceElectronics and Communications, Arab Academy for ScienceElectronics and Communications, Arab Academy for ScienceAbstract Invasive breast cancer diagnosis and treatment planning require an accurate assessment of human epidermal growth factor receptor 2 (HER2) expression levels. While immunohistochemical techniques (IHC) are the gold standard for HER2 evaluation, their implementation can be resource-intensive and costly. To reduce these obstacles and expedite the procedure, we present an efficient deep-learning model that generates high-quality IHC-stained images directly from Hematoxylin and Eosin (H&E) stained images. We propose a new IHC-GAN that enhances the Pix2PixHD model into a dual generator module, improving its performance and simplifying its structure. Furthermore, to strengthen feature extraction for HE-stained image classification, we integrate MobileNetV3 as the backbone network. The extracted features are then merged with those generated by the generator to improve overall performance. Moreover, the decoder’s performance is enhanced by providing the related features from the classified labels by incorporating the adaptive instance normalization technique. The proposed IHC-GAN was trained and validated on a comprehensive dataset comprising 4,870 registered image pairs, encompassing a spectrum of HER2 expression levels. Our findings demonstrate promising results in translating H&E images to IHC-equivalent representations, offering a potential solution to reduce the costs associated with traditional HER2 assessment methods. We extensively validate our model and the current dataset. We compare it with state-of-the-art techniques, achieving high performance using different evaluation metrics, showing 0.0927 FID, 22.87 PSNR, and 0.3735 SSIM. The proposed approach exhibits significant enhancements over current GAN models, including an 88% reduction in Frechet Inception Distance (FID), a 4% enhancement in Learned Perceptual Image Patch Similarity (LPIPS), a 10% increase in Peak Signal-to-Noise Ratio (PSNR), and a 45% reduction in Mean Squared Error (MSE). This advancement holds significant potential for enhancing efficiency, reducing manpower requirements, and facilitating timely treatment decisions in breast cancer care.https://doi.org/10.1186/s12880-024-01522-yBreast cancerIHCGANPix2Pix |
| spellingShingle | Afaf Saad Noha Ghatwary Safa M. Gasser Mohamed S. ElMahallawy Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model BMC Medical Imaging Breast cancer IHC GAN Pix2Pix |
| title | Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model |
| title_full | Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model |
| title_fullStr | Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model |
| title_full_unstemmed | Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model |
| title_short | Automatic image generation and stage prediction of breast cancer immunobiological through a proposed IHC-GAN model |
| title_sort | automatic image generation and stage prediction of breast cancer immunobiological through a proposed ihc gan model |
| topic | Breast cancer IHC GAN Pix2Pix |
| url | https://doi.org/10.1186/s12880-024-01522-y |
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