{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/axial-attention-based-explainability-for","title":"AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans","arxiv_id":"2407.02418","date":"2024-07-02","proceeding":null,"authors":["Gabriele Lozupone","Alessandro Bria","Francesco Fontanella","Frederick J. A. Meijer","Claudio De Stefano"],"abstract":"This study presents an innovative method for Alzheimer's disease diagnosis using 3D MRI designed to enhance the explainability of model decisions. Our approach adopts a soft attention mechanism, enabling 2D CNNs to extract volumetric representations. At the same time, the importance of each slice in decision-making is learned, allowing the generation of a voxel-level attention map to produce an explainable MRI. To test our method and ensure the reproducibility of our results, we chose a standardized collection of MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). On this dataset, our method significantly outperforms state-of-the-art methods in (i) distinguishing AD from cognitive normal (CN) with an accuracy of 0.856 and Matthew's correlation coefficient (MCC) of 0.712, representing improvements of 2.4% and 5.3% respectively over the second-best, and (ii) in the prognostic task of discerning stable from progressive mild cognitive impairment (MCI) with an accuracy of 0.725 and MCC of 0.443, showing improvements of 10.2% and 20.5% respectively over the second-best. We achieved this prognostic result by adopting a double transfer learning strategy, which enhanced sensitivity to morphological changes and facilitated early-stage AD detection. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions: the hippocampus, the amygdala, the parahippocampal, and the inferior lateral ventricles. All these areas are clinically associated with AD development. Furthermore, our approach consistently found the same AD-related areas across different cross-validation folds, proving its robustness and precision in highlighting areas that align closely with known pathological markers of the disease.","url_abs":"https://arxiv.org/abs/2407.02418v2","url_pdf":"https://arxiv.org/pdf/2407.02418v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"axial-attention-based-explainability-for","repo_url":"https://github.com/GabrieleLozupone/AXIAL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-classification","task_name":"3D Classification"},{"task_slug":"alzheimer-s-disease-detection","task_name":"Alzheimer's Disease Detection"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"xai","task_name":"Explainable Artificial Intelligence (XAI)"},{"task_slug":null,"task_name":"Hippocampus"},{"task_slug":"stable-mci-vs-progressive-mci","task_name":"Stable MCI vs Progressive MCI"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"align","method_name":"ALIGN"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/alzheimer-s-disease-detection-on-adni","task":"Alzheimer's Disease Detection","dataset":"ADNI","model":"AXIAL","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (5-fold)":"85.6%","MCC (5-fold)":"0.712"},"uses_additional_data":false},{"leaderboard":"/sota/explainable-artificial-intelligence-xai-on","task":"Explainable Artificial Intelligence (XAI)","dataset":"ADNI","model":"AXIAL","rank_in_archive_order":1,"of":1,"metrics":{"AD-Related Brain Areas Identified":"hippocampus, amygdala, parahippocampal, inferior lateral ventricles"},"uses_additional_data":false},{"leaderboard":"/sota/stable-mci-vs-progressive-mci-on-adni","task":"Stable MCI vs Progressive MCI","dataset":"ADNI","model":"AXIAL","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy (5-fold)":"72.5%","MCC (5-fold)":"0.443"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}