Stain Normalization of Hematology Slides using Neural Color Transfer
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910598840713216 |
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| author | Arshad, M. Muneeb Sajid, Hasan Khan, M. Jawad |
| author_facet | Arshad, M. Muneeb Sajid, Hasan Khan, M. Jawad |
| contents | Deep learning is popularly used for analyzing pathology images, but variations in image properties can limit the effectiveness of the models. The study aims to develop a method that transfers the variability present in the training set to unseen images, improving the model's ability to make accurate inferences. YOLOv5 was trained on peripheral blood and bone marrow sample images and Neural Color Transfer techniques were used to incorporate invariance. The results showed significant improvement in detecting WBCs from untrained samples after normalization, highlighting the potential of deep learning-based normalization techniques for inference robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_06742 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Stain Normalization of Hematology Slides using Neural Color Transfer Arshad, M. Muneeb Sajid, Hasan Khan, M. Jawad Image and Video Processing Deep learning is popularly used for analyzing pathology images, but variations in image properties can limit the effectiveness of the models. The study aims to develop a method that transfers the variability present in the training set to unseen images, improving the model's ability to make accurate inferences. YOLOv5 was trained on peripheral blood and bone marrow sample images and Neural Color Transfer techniques were used to incorporate invariance. The results showed significant improvement in detecting WBCs from untrained samples after normalization, highlighting the potential of deep learning-based normalization techniques for inference robustness. |
| title | Stain Normalization of Hematology Slides using Neural Color Transfer |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2409.06742 |