Interpretable multimodal classification for age-related macular degeneration diagnosis
Year: 2024
Type: article
Abstract: Explainable Artificial Intelligence (XAI) is an emerging machine learning field that has been successful in medical image analysis. Interpretable approaches are able to "unbox" the black-box decisions made by AI systems, aiding medical doctors to justify their diagnostics better. In this paper, we analyze the performance of three different XAI strategies for medical image analysis in ophthalmology. We consider a multimodal deep learning model that combines optical coherence tomography (OCT) and infrared reflectance (IR) imaging for the diagnosis of age-related macular degeneration (AMD). The classification model is able to achieve an accuracy of 0.94, performing better than other unimodal alternatives. We analyze the XAI methods in terms of their ability to identify retinal damage and ease of interpretation, concluding that grad-CAM and guided grad-CAM can be combined to have both a coarse visual justification and a fine-grained analysis of the retinal layers. We provide important insights and recommendations for practitioners on how to design automated and explainable screening tests based on the combination of two image sources.
Source: PLoS ONE
Language: English
Topic: Retinal Imaging and Analysis
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,290
Awards: INSTITUTO SISTEMAS COMPLEJOS DE INGENIERIA, ROLE OF MKP-1 MODULATING THE IMMUNE RESPONSE IN VOGT-KOYANAGI-HARADA DISEASE IMPLICATIONS ON EARLY IDENTIFICATION OF INFLAMMATORY ACTIVITY, ROLE OF MIRNAS AS CD4+ T CELLS REGULATORS IN VOGT-KOYANAGI-HARADA DISEASE CLINICAL IMPLICATIONS AS NOVEL BIOMARKERS OF INFLAMMATORY ACTIVITY., IMPROVING OUT-OF-TIME PREDICTIVE PERFORMANCE IN BUSINESS ANALYTICS VIA ROBUST OPTIMIZATION AND PROFIT METRICS, G3588573508, G3653195308, G7081644260, G8061419107, G8231673853
OpenAlex ID: W4404240969