| LOBASKIN EGOR | D, 中間発表 | 数理情報学 | 池田 和司☆, | 川鍋 一晃, | 杉本 徳和, | 田中 沙織 |
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title: Metric learning of Trans-diagnostic Biomarkers from Human Brain Functional Connectivity
abstract: Advances in machine learning have transformed data-rich clinical domains such as genomics and radiology, yet psychiatry lags in the clinical adoption of computational methods. Neuroimaging in mental health faces three major structural constraints: severe data scarcity relative to dimensionality, cross-site heterogeneity in image acquisition, and ethical concerns around deploying opaque AI in sensitive clinical contexts. We propose a geometry-aware framework that replaces reductive classification with metric learning in the spectral domain of functional connectivity matrices, mapping patients into a continuous transdiagnostic landscape that respects symptomatic overlaps between conditions as well as individual variation. Leveraging Fourier analysis on the graph Laplacian, we learn spectral filters that isolate pathology-relevant network modes, while adversarial regularization ensures embeddings remain grounded in clinical plausibility. Critically, we uncover a persistent confound: site and protocol biases survive both geometric alignment and regression-based harmonization, manifesting in eigenvector orientations. This reveals a blind spot in current practices and calls for spectral-aware approaches to domain adaptation. language of the presentation: English | ||||||
| 児玉 創太郎 | M, 2回目発表 | 脳・行動モデリング | 田中 沙織, | 池田 和司, | 久保 孝富, | 荻島 大凱 |
| 佐野 海士 | D, 中間発表 | 脳・行動モデリング | 田中 沙織, | 池田 和司, | CAI LIN, | 荻島 大凱 |
| WEI XUEFENG | D, 中間発表 | 自然言語処理学 | 渡辺 太郎, | Sakriani Sakti, | 上垣外 英剛, | 坂井 優介 |
| ZHOU XUAN | D, 中間発表 | 自然言語処理学 | 渡辺 太郎, | Sakriani Sakti, | 上垣外 英剛, | 坂井 優介 |
| YU SHUYI | M, 2回目発表 | 自然言語処理学 | 渡辺 太郎, | Sakriani Sakti, | 上垣外 英剛, | 坂井 優介 |