Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and dermatologist evaluation
Victor Liang, Parasto Shahrouki, Daniel Nejad, Eva Backman, Magdalena Claeson, Åsa Ingvar, Isabelle Krakowski, Kari Nielsen, Jenna Pakka, John Paoli, Niki Radros, Mari Salmivuori, Sam Polesie · 2026
Read Assessment of high- and low-risk histopathologic subtypes of basal cell carcinoma using artificial intelligence and dermatologist evaluation by Victor Liang, Parasto Shahrouki, Daniel Nejad, Eva Backman, Magdalena Claeson, Åsa Ingvar, Isabelle Krakowski, Kari Nielsen, Jenna Pakka, John Paoli, Niki Radros, Mari Salmivuori, Sam Polesie (2026) 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-13196092.txt.
Description
BackgroundBasal cell carcinoma (BCC) is the most common skin cancer, and accurate histopathologic risk stratification is essential for treatment selection. Convolutional neural networks (CNNs) demonstrate promise in image analysis, yet few studies have evaluated classification of BCC subtypes.ObjectiveTo assess the diagnostic performance of a CNN for binary classification of high-versus low-risk histopathologic subtypes of BCC and compare it with dermatologists.
Catalog record
| CREATOR | Victor Liang, Parasto Shahrouki, Daniel Nejad, Eva Backman, Magdalena Claeson, Åsa Ingvar, Isabelle Krakowski, Kari Nielsen, Jenna Pakka, John Paoli, Niki Radros, Mari Salmivuori, Sam Polesie |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 30 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |