Electrical Engineering and Systems Science > Image and Video Processing
arXiv:2403.01758 (eess)
[Submitted on 4 Mar 2024 (v1), last revised 24 Aug 2024 (this version, v2)]
Title:GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity
View a PDF of the paper titled GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity, by Xiongri Shen and 2 other authors
View PDFHTML (experimental)Abstract:Diagnosis of mild cognitive impairment (MCI) and subjective cognitive decline (SCD) from fMRI functional connectivity (FC) has gained popularity, but most FC-based diagnostic models are black boxes lacking casual reasoning so they contribute little to the knowledge about FC-based neural biomarkers of cognitivethis http URL enhance the explainability of diagnostic models, we propose a generative counterfactual attention-guided network (GCAN), which introduces counterfactual reasoning to recognize cognitive decline-related brain regions and then uses these regions as attention maps to boost the prediction performance of diagnostic models. Furthermore, to tackle the difficulty in the generation of highly-structured and brain-atlas-constrained FC, which is essential in counterfactual reasoning, an Atlas-Aware Bidirectional Transformer (AABT) method is developed. AABT employs a bidirectional strategy to encode and decode the tokens from each network of brain atlas, thereby enhancing the generation of high-quality target label FC. In the experiments of hospital-collected and ADNI datasets, the generated attention maps closely resemble FC abnormalities in the literature on SCD and MCI. The diagnostic performance is also superior to baseline models. The code is available atthis https URL
| Comments: | 10 pages, 5 figures |
| Subjects: | Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG) |
| Report number: | accept by MICCAI2024 |
| Cite as: | arXiv:2403.01758 [eess.IV] |
| (orarXiv:2403.01758v2 [eess.IV] for this version) | |
| https://doi.org/10.48550/arXiv.2403.01758 arXiv-issued DOI via DataCite | |
| Related DOI: | https://doi.org/10.1007/978-3-031-72117-5_39 DOI(s) linking to related resources |
Submission history
From: Shen Xiongri [view email][v1] Mon, 4 Mar 2024 06:24:24 UTC (1,389 KB)
[v2] Sat, 24 Aug 2024 11:36:15 UTC (1,832 KB)
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View a PDF of the paper titled GCAN: Generative Counterfactual Attention-guided Network for Explainable Cognitive Decline Diagnostics based on fMRI Functional Connectivity, by Xiongri Shen and 2 other authors
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