A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study

• URL: http://arxiv.org/abs/2601.16467v1
• Date: Fri, 23 Jan 2026 05:55:36 GMT
• Title: A Cautionary Tale of Self-Supervised Learning for Imaging Biomarkers: Alzheimer's Disease Case Study
• Authors: Maxwell Reynolds, Chaitanya Srinivasan, Vijay Cherupally, Michael Leone, Ke Yu, Li Sun, Tigmanshu Chaudhary, Andreas Pfenning, Kayhan Batmanghelich,
• Abstract summary: Self-supervised learning can uncover more powerful biomarkers from the same data.<n>We introduce Residual Noise Contrastive Estimation (R-NCE), a new SSL framework that integrates auxiliary FreeSurfer features.<n>R-NCE outperforms traditional features and existing SSL methods across multiple benchmarks, including Alzheimer's disease conversion prediction.
• Score: 11.877798251991473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Discovery of sensitive and biologically grounded biomarkers is essential for early detection and monitoring of Alzheimer's disease (AD). Structural MRI is widely available but typically relies on hand-crafted features such as cortical thickness or volume. We ask whether self-supervised learning (SSL) can uncover more powerful biomarkers from the same data. Existing SSL methods underperform FreeSurfer-derived features in disease classification, conversion prediction, and amyloid status prediction. We introduce Residual Noise Contrastive Estimation (R-NCE), a new SSL framework that integrates auxiliary FreeSurfer features while maximizing additional augmentation-invariant information. R-NCE outperforms traditional features and existing SSL methods across multiple benchmarks, including AD conversion prediction. To assess biological relevance, we derive Brain Age Gap (BAG) measures and perform genome-wide association studies. R-NCE-BAG shows high heritability and associations with MAPT and IRAG1, with enrichment in astrocytes and oligodendrocytes, indicating sensitivity to neurodegenerative and cerebrovascular processes.

Related papers

• Investigating the Impact of Histopathological Foundation Models on Regressive Prediction of Homologous Recombination Deficiency [52.50039435394964]
  We systematically evaluate foundation models for regression-based tasks.<n>We extract patch-level features from whole slide images (WSI) using five state-of-the-art foundation models.<n>Models are trained to predict continuous HRD scores based on these extracted features across breast, endometrial, and lung cancer cohorts.
  arXiv  Detail & Related papers  (2026-01-29T14:06:50Z)
• MethConvTransformer: A Deep Learning Framework for Cross-Tissue Alzheimer's Disease Detection [4.931890971425293]
  Alzheimer's disease (AD) is a multifactorial neurodegenerative disorder characterized by progressive cognitive decline and widespread dysregulation in the brain.<n>MethConvTransformer is a transformer-based deep learning framework that integrates DNA methylation profiles from both brain and peripheral tissues.
  arXiv  Detail & Related papers  (2026-01-01T00:18:33Z)
• R-GenIMA: Integrating Neuroimaging and Genetics with Interpretable Multimodal AI for Alzheimer's Disease Progression [63.97617759805451]
  Early detection of Alzheimer's disease requires models capable of integrating macro-scale neuroanatomical alterations with micro-scale genetic susceptibility.<n>We introduce R-GenIMA, an interpretable multimodal large language model that couples a novel ROI-wise vision transformer with genetic prompting.<n>R-GenIMA achieves state-of-the-art performance in four-way classification across normal cognition, subjective memory concerns, mild cognitive impairment, and AD.
  arXiv  Detail & Related papers  (2025-12-22T02:54:10Z)
• An Interpretable Ensemble Framework for Multi-Omics Dementia Biomarker Discovery Under HDLSS Conditions [0.0]
  We propose a novel ensemble approach combining Graph Attention Networks (GAT), MultiOmics Variational AutoEncoder (MOVE), Elastic-net sparse regression, and Storey's False Discovery Rate (FDR)<n>We evaluate performance using both simulated multi-omics data and the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.<n>Our method demonstrates superior predictive accuracy, feature selection precision, and biological relevance.
  arXiv  Detail & Related papers  (2025-09-04T15:20:13Z)
• Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes [16.100668392804476]
  Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world.<n>Brain structural magnetic resonance imaging (sMRI) may provide a safer and more convenient solution for the AD diagnosis.<n>Recent advances in geometric deep learning have facilitated sMRI analysis and early diagnosis of AD.
  arXiv  Detail & Related papers  (2025-03-06T23:02:18Z)
• Dynamic Classification of Latent Disease Progression with Auxiliary Surrogate Labels [4.997489272248076]
  Disease progression prediction based on evolving health information is challenging when true disease states are unknown.<n>We develop an adaptive forward-backward algorithm with subjective labels for estimation.<n>Asymptotic properties are established, and significant improvement with finite samples is demonstrated.
  arXiv  Detail & Related papers  (2024-12-11T04:14:15Z)
• Assessing the Efficacy of Classical and Deep Neuroimaging Biomarkers in Early Alzheimer's Disease Diagnosis [2.2667044928324747]
  Alzheimer's disease (AD) is the leading cause of dementia, and its early detection is crucial for effective intervention. This study aims to detect significant indicators of early AD by extracting and integrating various imaging biomarkers.
  arXiv  Detail & Related papers  (2024-10-31T15:02:16Z)
• On the Within-class Variation Issue in Alzheimer's Disease Detection [66.70233526150723]
  Alzheimer's Disease (AD) detection employs machine learning classification models to distinguish between individuals with AD and those without.<n>In this work, we found using a sample score estimator can generate sample-specific soft scores aligning with cognitive scores.<n>We propose two simple yet effective methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe)
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• AXIAL: Attention-based eXplainability for Interpretable Alzheimer's Localized Diagnosis using 2D CNNs on 3D MRI brain scans [43.06293430764841]
  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. With voxel-level precision, our method identified which specific areas are being paid attention to, identifying these predominant brain regions.
  arXiv  Detail & Related papers  (2024-07-02T16:44:00Z)
• An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer's disease [13.213387075528017]
  Alzheimer's disease (AD) is the most prevalent form of dementia worldwide, encompassing a prodromal stage known as Mild Cognitive Impairment (MCI)<n>The objective of the work was to capture structural and functional modulations of brain structure and function relying on multimodal MRI data and Single Nucleotide Polymorphisms.
  arXiv  Detail & Related papers  (2024-06-19T07:31:47Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• G-MIND: An End-to-End Multimodal Imaging-Genetics Framework for Biomarker Identification and Disease Classification [49.53651166356737]
  We propose a novel deep neural network architecture to integrate imaging and genetics data, as guided by diagnosis, that provides interpretable biomarkers. We have evaluated our model on a population study of schizophrenia that includes two functional MRI (fMRI) paradigms and Single Nucleotide Polymorphism (SNP) data.
  arXiv  Detail & Related papers  (2021-01-27T19:28:04Z)
• A Graph Gaussian Embedding Method for Predicting Alzheimer's Disease Progression with MEG Brain Networks [59.15734147867412]
  Characterizing the subtle changes of functional brain networks associated with Alzheimer's disease (AD) is important for early diagnosis and prediction of disease progression. We developed a new deep learning method, termed multiple graph Gaussian embedding model (MG2G) We used MG2G to detect the intrinsic latent dimensionality of MEG brain networks, predict the progression of patients with mild cognitive impairment (MCI) to AD, and identify brain regions with network alterations related to MCI.
  arXiv  Detail & Related papers  (2020-05-08T02:29:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.