VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology
Muhammad Altaf Hussain, Muhammad Asim Waris, Muhammad Usman Akram, Muhammad Jawad Khan, Muhammad Zeeshan Asaf, Amber Javaid, Fariha Sahrish, Syed Omer Gilani, Fawwaz Hazzazi · 2025
Read VISGAB: Virtual staining-driven GAN benchmarking for optimizing skin tissue histology by Muhammad Altaf Hussain, Muhammad Asim Waris, Muhammad Usman Akram, Muhammad Jawad Khan, Muhammad Zeeshan Asaf, Amber Javaid, Fariha Sahrish, Syed Omer Gilani, Fawwaz Hazzazi (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12660401.txt.
Description
Muhammad Altaf Hussain and co-authors benchmark AI systems that paint tissue stains digitally instead of chemically, in a 2025 paper. The standard stain for skin samples is slow, costly, and uneven between technicians. Their benchmark, VISGAB, pits three generative network designs against each other on a shared set of skin slides covering normal tissue, cancers, and inflammation.
The team adds a scoring index that prizes diagnostic value over mere looks. One design, CycleGAN, won on structural fidelity and on finding abnormal nuclei, and it fooled experts most often in a Turing-style test. The rivals trained faster but blurred or invented detail, limiting their use. The authors back digital staining with diagnostic checks built in.
Catalog record
| CREATOR | Muhammad Altaf Hussain, Muhammad Asim Waris, Muhammad Usman Akram, Muhammad Jawad Khan, Muhammad Zeeshan Asaf, Amber Javaid, Fariha Sahrish, Syed Omer Gilani, Fawwaz Hazzazi |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 75 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |