Clinical validation of saliency maps for understanding deep neural networks in ophthalmology
| Autor: | Laura Kühlewein, Gulnar Aliyeva, Murat Seckin Ayhan, Focke Ziemssen, Philipp Berens, Werner Inhoffen, Louis Benedikt Kümmerle |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Network architecture
Medical diagnostic medicine.medical_specialty Diabetic Retinopathy medicine.diagnostic_test Point (typography) Radiological and Ultrasound Technology Fundus Oculi Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Health Informatics Deep Neural Networks Neovascular Age-related Macular Degeneration Saliency Maps Computer Graphics and Computer-Aided Design Ophthalmology Optical coherence tomography medicine Humans Deep neural networks Radiology Nuclear Medicine and imaging Neural Networks Computer Computer Vision and Pattern Recognition Set (psychology) Tomography Optical Coherence |
| Zdroj: | Med. Image Anal. 77:102364 (2022) |
| Popis: | 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 alle-viate 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. |
| Databáze: | OpenAIRE |
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