Dermatological Diagnosis Explainability Benchmark for Convolutional Neural Networks

1 code implementation • 23 Feb 2023 • Raluca Jalaboi, Ole Winther, Alfiia Galimzianova

We pre-trained all architectures on an clinical skin disease dataset, and fine-tuned them on a DermXDB subset.

Benchmarking Medical Diagnosis

Explainable Image Quality Assessments in Teledermatological Photography

no code implementations • 10 Sep 2022 • Raluca Jalaboi, Ole Winther, Alfiia Galimzianova

For poor image quality explanations, our method obtains F1-scores of between 0. 37 +- 0. 01 and 0. 70 +- 0. 01, similar to the inter-rater pairwise F1-score of between 0. 24 +- 0. 15 and 0. 83 +- 0. 06.

Image Quality Assessment

DermX: an end-to-end framework for explainable automated dermatological diagnosis

1 code implementation • 14 Feb 2022 • Raluca Jalaboi, Frederik Faye, Mauricio Orbes-Arteaga, Dan Jørgensen, Ole Winther, Alfiia Galimzianova

We assess the explanation performance in terms of identification and localization by comparing model-selected with dermatologist-selected explanations, and gradient-weighted class-activation maps with dermatologist explanation maps, respectively.