Clinical validation of saliency maps for understanding deep neural networks in ophthalmology.
Med Image Anal
; 77: 102364, 2022 04.
Article
in En
| MEDLINE
| ID: mdl-35101727
ABSTRACT
Deep neural networks (DNNs) have achieved physician-level accuracy on many imaging-based medical diagnostic tasks, for example classification of retinal images in ophthalmology. However, their decision mechanisms are often considered impenetrable leading to a lack of trust by clinicians and patients. To alleviate this issue, a range of explanation methods have been proposed to expose the inner workings of DNNs leading to their decisions. For imaging-based tasks, this is often achieved via saliency maps. The quality of these maps are typically evaluated via perturbation analysis without experts involved. To facilitate the adoption and success of such automated systems, however, it is crucial to validate saliency maps against clinicians. In this study, we used three different network architectures and developed ensembles of DNNs to detect diabetic retinopathy and neovascular age-related macular degeneration from retinal fundus images and optical coherence tomography scans, respectively. We used a variety of explanation methods and obtained a comprehensive set of saliency maps for explaining the ensemble-based diagnostic decisions. Then, we systematically validated saliency maps against clinicians through two main analyses - a direct comparison of saliency maps with the expert annotations of disease-specific pathologies and perturbation analyses using also expert annotations as saliency maps. We found the choice of DNN architecture and explanation method to significantly influence the quality of saliency maps. Guided Backprop showed consistently good performance across disease scenarios and DNN architectures, suggesting that it provides a suitable starting point for explaining the decisions of DNNs on retinal images.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
Ophthalmology
/
Diabetic Retinopathy
Type of study:
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Med Image Anal
Journal subject:
DIAGNOSTICO POR IMAGEM
Year:
2022
Document type:
Article
Affiliation country:
Alemania