An unsupervised feature learning framework for basal cell carcinoma image analysis

Conclusions The proposed UFL-representation-based approach outperforms state-of-the-art methods for BCC detection. Thanks to its visual interpretation layer, the method is able to highlight discriminative tissue regions providing a better diagnosis support. Among the different UFL strategies tested, TICA-learned features exhibited the best performance thanks to its ability to capture low-level invariances, which are inherent to the nature of the problem.
Source: Artificial Intelligence in Medicine - Category: Bioinformatics Source Type: research