Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection

• URL: http://arxiv.org/abs/2504.03230v2
• Date: Fri, 25 Apr 2025 18:54:01 GMT
• Title: Unlocking Neural Transparency: Jacobian Maps for Explainable AI in Alzheimer's Detection
• Authors: Yasmine Mustafa, Mohamed Elmahallawy, Tie Luo,
• Abstract summary: This paper introduces a novel pre-model approach leveraging Jacobian Maps (JMs) within a multi-modal framework to enhance explainability and trustworthiness in Alzheimer's disease detection.<n>We validate JMs through experiments comparing a 3D CNN trained on JMs versus on traditional preprocessed data, which demonstrates superior accuracy.<n>We also employ 3D Grad-CAM analysis to provide both visual and quantitative insights, further showcasing improved interpretability and diagnostic reliability.
• Score: 1.1674893622721483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alzheimer's disease (AD) leads to progressive cognitive decline, making early detection crucial for effective intervention. While deep learning models have shown high accuracy in AD diagnosis, their lack of interpretability limits clinical trust and adoption. This paper introduces a novel pre-model approach leveraging Jacobian Maps (JMs) within a multi-modal framework to enhance explainability and trustworthiness in AD detection. By capturing localized brain volume changes, JMs establish meaningful correlations between model predictions and well-known neuroanatomical biomarkers of AD. We validate JMs through experiments comparing a 3D CNN trained on JMs versus on traditional preprocessed data, which demonstrates superior accuracy. We also employ 3D Grad-CAM analysis to provide both visual and quantitative insights, further showcasing improved interpretability and diagnostic reliability.

Related papers

• Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction [45.89562183034469]
  Existing deep learning diagnosis prediction models with intrinsic interpretability often assign attention weights to every past diagnosis or hospital visit.<n>We introduce SHy, a self-explaining hypergraph neural network model, designed to offer personalized, concise and faithful explanations.<n> SHy captures higher-order disease interactions and extracts distinct temporal phenotypes as personalized explanations.
  arXiv  Detail & Related papers  (2025-02-15T06:33:02Z)
• MINDSETS: Multi-omics Integration with Neuroimaging for Dementia Subtyping and Effective Temporal Study [0.7751705157998379]
  Alzheimer's disease (AD) and vascular dementia (VaD) are the two most prevalent dementia types. This paper presents an innovative multi-omics approach to accurately differentiate AD from VaD, achieving a diagnostic accuracy of 89.25%.
  arXiv  Detail & Related papers  (2024-11-06T10:13:28Z)
• Towards Within-Class Variation in Alzheimer's Disease Detection from Spontaneous Speech [60.08015780474457]
  Alzheimer's Disease (AD) detection has emerged as a promising research area that employs machine learning classification models. We identify within-class variation as a critical challenge in AD detection: individuals with AD exhibit a spectrum of cognitive impairments. We propose two novel methods: Soft Target Distillation (SoTD) and Instance-level Re-balancing (InRe), targeting two problems respectively.
  arXiv  Detail & Related papers  (2024-09-22T02:06:05Z)
• Unsupervised Analysis of Alzheimer's Disease Signatures using 3D Deformable Autoencoders [10.091922917520316]
  We present MORPHADE, a novel unsupervised learning approach which uses deformations to allow the analysis of 3D T1-weighted brain images. This is the first use of deformations with deep unsupervised learning to detect, but also localize and assess the severity of structural changes in the brain due to Alzheimer's Disease (AD) Our method achieves an AUROC of 0.80 in detecting AD, out-performing several supervised and unsupervised baselines.
  arXiv  Detail & Related papers  (2024-07-04T11:52:44Z)
• 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)
• Enhancing MRI-Based Classification of Alzheimer's Disease with Explainable 3D Hybrid Compact Convolutional Transformers [13.743241062824548]
  Alzheimer's disease (AD) presents a formidable global health challenge. Traditional analysis methods often struggle to discern intricate 3D patterns crucial for AD identification. We introduce the 3D Hybrid Compact Convolutional Transformers 3D (HCCT)
  arXiv  Detail & Related papers  (2024-03-24T14:35:06Z)
• Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision [1.5501208213584152]
  We introduce an interpretable, multimodal model for Alzheimer's disease (AD) classification over its multi-stage progression, incorporating Jacobian Saliency Map (JSM) as a modality-agnostic tool. Our evaluation including ablation study manifests the efficacy of using JSM for model debug and interpretation, while significantly enhancing model accuracy as well.
  arXiv  Detail & Related papers  (2024-02-25T06:53:35Z)
• Automatic diagnosis of knee osteoarthritis severity using Swin transformer [55.01037422579516]
  Knee osteoarthritis (KOA) is a widespread condition that can cause chronic pain and stiffness in the knee joint. We propose an automated approach that employs the Swin Transformer to predict the severity of KOA.
  arXiv  Detail & Related papers  (2023-07-10T09:49:30Z)
• Leveraging Pretrained Representations with Task-related Keywords for Alzheimer's Disease Detection [69.53626024091076]
  Alzheimer's disease (AD) is particularly prominent in older adults. Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations. This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
  arXiv  Detail & Related papers  (2023-03-14T16:03:28Z)
• An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI [22.34325971680329]
  We have proposed a novel computer-aided approach for early diagnosis of Alzheimer's disease by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. The experimental results show that the proposed approach has a competitive advantage over the state-of-the-art models in terms of accuracy performance and generalizability.
  arXiv  Detail & Related papers  (2020-08-10T11:08:55Z)
• 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.