1 citations · 1 across the 3 of their papers we have counts for
3 papers
Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks
Raluca Jalaboi, Ole Winther, Alfiia Galimzianova
In recent years, large strides have been taken in developing machine learning methods for dermatological applications, supported in part by the success of deep learning (DL). To da…
Explainable Image Quality Assessments in Teledermatological Photography
Raluca Jalaboi, Ole Winther, Alfiia Galimzianova
Image quality is a crucial factor in the effectiveness and efficiency of teledermatological consultations. However, up to 50% of images sent by patients have quality issues, thus i…
DermX: an end-to-end framework for explainable automated dermatological diagnosis
Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga +3
Dermatological diagnosis automation is essential in addressing the high prevalence of skin diseases and critical shortage of dermatologists. Despite approaching expert-level diagno…