LesionMap: A Method and Tool for the Semantic Annotation of Dermatological Lesions for Documentation and Machine Learning

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 a simple method—LesionMap—and an electronic health software tool—LesionMapper—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. We believe that LesionMapper will facilitate documentation in dermatology that can be used for machine learning in a privacy-preserving manner.

Fostering Apprentice Beauticians’ Visual Expertise Through Annotations: A Design Experiment Using the Platform Realto

“…− The training material created for the present study combines elements of other trainings that already proved to be effective in other fields, such as1) the use of attention-guiding cues to train students' gaze (e.g., Boucheix & Lowe, 2010), 2) the use of technology-supported annotations to help professional dermatologists with skin anomalies (e.g., Ferreira et al, 2012;Eapen et al, 2020) and 3) repeated sets of images (e.g., Rimoin et al, 2015).…”

“…One example is a tool containing manual segmentation, labelling and image manipulation functions (Ferreira et al, 2012). As a comprehensive tool, LesionMap contains the functions needed to indicate information about anomalies, such as orientation, width, position and opacity (Figure 2; Eapen et al, 2020). Eapen et al, 2012 How to train novices to develop their visual expertise skills, however, is still a question worth investigating.…”

OCT-specific signal features for semi-automatic semantic scans annotation and segmentations

“…characterized by low precision of isolation). To increase the efficiency of this procedure one can use semi-automatic semantic annotation tools 1,2,3,4 where some simplified and manually tuned approaches are already implemented to isolate specific objects. These semi-automatic tools work in the human-in-theloop (HITL) paradigm 5 , where each automatically annotated image is assessed by a human and if the annotation fails, the operator can tune the parameters of the annotation tool or correct the output manually.…”

DICODerma: A Practical Approach for Metadata Management of Images in Dermatology

“…The lack of a well-established standard has an impact on patient privacy as well [ 2 ]. Dermatologists do not have standards-based solutions such as the Picture Archival and Retrieval System (PACS) to rely on for sharing images among them and peers.…”