Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal
Lennart Jütte, Harshkumar Patel, Bernhard Roth · 2024
Read Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal by Lennart Jütte, Harshkumar Patel, Bernhard Roth (2024) 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-11575456.txt.
Description
Lennart Jütte, Harshkumar Patel, and Bernhard Roth tackle, in a 2024 study, a common flaw in skin scope images used to spot melanoma: hair crossing the lesion. Hair can hide key features and slow diagnosis. Real image sets with known hair are scarce, so the team built a synthetic set that adds varied hair to lesion images.
They then trained a neural network on that set to erase the hair while keeping the lesion intact. The cleaned images were clearer and more useful for diagnosis, the authors report. The data set and model give other groups a shared test bed. Better hair removal could support earlier and more accurate melanoma checks.
Catalog record
| CREATOR | Lennart Jütte, Harshkumar Patel, Bernhard Roth |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 43 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |