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Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

Saliency-Enhanced Content-Based Image Retrieval for Diagnosis Support in Dermatology Consultation: Reader Study

To evaluate the retrieval performance and compare the traditional CBIR algorithm against SE-CBIR, we used the retrieval precision for k retrieved images (P@k) or cut-off value metric: Similarly, for multiclass retrieval, we defined the average precision for k retrieved images (AP@k): with M being the total number of classes. To evaluate the impact of this type of decision support, a reader study was designed within the University Hospital Zurich Dermatology Clinic.

Mathias Gassner, Javier Barranco Garcia, Stephanie Tanadini-Lang, Fabio Bertoldo, Fabienne Fröhlich, Matthias Guckenberger, Silvia Haueis, Christin Pelzer, Mauricio Reyes, Patrick Schmithausen, Dario Simic, Ramon Staeger, Fabio Verardi, Nicolaus Andratschke, Andreas Adelmann, Ralph P Braun

JMIR Dermatol 2023;6:e42129

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

The 9 superfeatures (dots, globules, lines, network structures, patterns, regression structures, shiny white structures, structureless areas, and vessels; shown in Multimedia Appendix 1, Table S1) were presented to participants during the tutorial on the Diagnos Us smartphone app, adapted from Marghoob and Braun [41]. To measure agreement across raters, we employed the Cohen κ [42], which has a value of 0 for completely random choices, increasing toward a maximum value of 1.0 with improved IRR.

Jonathan Kentley, Jochen Weber, Konstantinos Liopyris, Ralph P Braun, Ashfaq A Marghoob, Elizabeth A Quigley, Kelly Nelson, Kira Prentice, Erik Duhaime, Allan C Halpern, Veronica Rotemberg

JMIR Med Inform 2023;11:e38412