| Autor: |
Muhammad Zeeshan Asaf, Anum Abdul Salam, Samavia Khan, Noah Musolff, Muhammad Usman Akram, Babar Rao |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Data in Brief, Vol 57, Iss , Pp 110997- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2352-3409 |
| DOI: |
10.1016/j.dib.2024.110997 |
| Popis: |
In the era of artificial intelligence and machine learning, computer-aided diagnostic frameworks are data-hungry and require large amounts of annotated data to automate the disease diagnosis procedure. Moreover, to enhance the performance and accuracy of disease diagnosis, procedures need to be automated to ensure timely and accurate diagnosis. We are providing a whole slide image repository comprising unstained skin biopsy images acquired using a brightfield microscope, along with Hematoxylin and Eosin chemically and virtually stained image samples, to virtualize the staining procedure and enhance the efficiency of the disease diagnosis pipeline. The dataset was utilized to train a Dual Contrastive GAN to generate virtually stained image samples. The trained model achieved an FID score of 80.47 between virtually stained and chemically stained image samples, indicating a high correlation of content between synthesized and original images. In contrast, FID scores of 342.01 and 320.40 were observed between unstained images and virtually stained slides, and between unstained images and chemically stained images, respectively, indicating less similarity in content. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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