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LesionMap: A method and a tool for the semantic annotation of dermatological lesions for documentation and machine learning
Bell Raj Eapen;
Norm Archer;
Kamran Sartipi
ABSTRACT
Diagnosis and follow-up of patients in dermatology rely on visual cues. Documentation of skin lesions in dermatology is time-consuming and inaccurate. Digital photography is resource-intensive, difficult to standardize and has privacy concerns. We propose Lesion- Mapper (LMR) — a method and a tool for semantically annotating dermatological lesions on a body wireframe. We discuss how the type, distribution and progression of lesions can be represented in a standardized way. The tool is an open-source javascript package that can be integrated into web-based electronic medical records (EMRs). We believe that LMR will facilitate documentation in dermatology that can be used for machine learning in a privacy-preserving manner.
Citation
Please cite as:
Eapen BR, Archer N, Sartipi K
LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning