Scalable, Axiomatic Explanations of Deep Alzheimer’s Diagnosis from Heterogeneous Data

Pölsterl, Sebastian; Aigner, Christina; Wachinger, Christian

doi:10.1007/978-3-030-87199-4_41

Cited by 5 publications

(4 citation statements)

References 20 publications

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“…However, given that cognitive tests are designed specifically to be used as dementia biomarkers, it is unsurprising that these are highlighted by predictive models for classification tasks. On the other hand, Polsterl and colleagues noted that whilst clinical variables were as relevant as hippocampal shape in most cases, there were a few exceptions amongst Alzheimer's Disease patients, demonstrating subject‐level variability 48 …”

Section: Resultsmentioning

confidence: 99%

“…On the other hand, Polsterl and colleagues noted that whilst clinical variables were as relevant as hippocampal shape in most cases, there were a few exceptions amongst Alzheimer's Disease patients, demonstrating subject‐level variability. 48 …”

Section: Resultsmentioning

confidence: 99%

“…Polsterl and colleagues noted that whilst clinical variables were as relevant as hippocampal shape in most cases, there were a few exceptions amongst Alzheimer's Disease patients, demonstrating subject-level variability. 48 For other diagnoses such as frontotemporal dementia, Hu and colleagues identified the right frontal white matter, left temporal, bilateral inferior frontal, and parahippocampal regions as valuable for prediction. 44 The model used by Morales highlighted cerebral white matter and volumes of the lateral ventricles and hippocampi as most relevant to dementia in Parkinson's disease.…”

Section: Model Interpretabilitymentioning

confidence: 99%

See 2 more Smart Citations

Interpretable machine learning for dementia: A systematic review

Martin

Townend

Barkhof

et al. 2023

Alzheimer's & Dementia

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Introduction:Machine learning research into automated dementia diagnosis is becoming increasingly popular but so far has had limited clinical impact. A key challenge is building robust and generalizable models that generate decisions that can be reliably explained. Some models are designed to be inherently "interpretable," whereas post hoc "explainability" methods can be used for other models.Methods: Here we sought to summarize the state-of-the-art of interpretable machine learning for dementia. Results:We identified 92 studies using PubMed, Web of Science, and Scopus. Studies demonstrate promising classification performance but vary in their validation procedures and reporting standards and rely heavily on popular data sets.Discussion: Future work should incorporate clinicians to validate explanation methods and make conclusive inferences about dementia-related disease pathology. Critically analyzing model explanations also requires an understanding of the interpretability methods itself. Patient-specific explanations are also required to demonstrate the benefit of interpretable machine learning in clinical practice.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Model Interpretabilitymentioning

confidence: 99%

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Interpretable machine learning for dementia: A systematic review

Martin

Townend

Barkhof

et al. 2023

Alzheimer's & Dementia

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“…As there is no cure available yet, detecting the disease as early as possible, in prodromal stages, is crucial for slowing down its progression and for patient to improve quality of life. The body of literature has show the potentials of existing machine learning methods e.g., [28,20,30,26]. However, there are two major limitations of existing techniques.…”

Section: Introductionmentioning

confidence: 99%

Multi-Modal Hypergraph Diffusion Network with Dual Prior for Alzheimer Classification

Avilés-Rivero¹,

Runkel²,

Papadakis³

et al. 2022

Preprint

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The automatic early diagnosis of prodromal stages of Alzheimer's disease is of great relevance for patient treatment to improve quality of life. We address this problem as a multi-modal classification task. Multi-modal data provides richer and complementary information. However, existing techniques only consider either lower order relations between the data and single/multi-modal imaging data. In this work, we introduce a novel semi-supervised hypergraph learning framework for Alzheimer's disease diagnosis. Our framework allows for higher-order relations among multi-modal imaging and non-imaging data whilst requiring a tiny labelled set. Firstly, we introduce a dual embedding strategy for constructing a robust hypergraph that preserves the data semantics. We achieve this by enforcing perturbation invariance at the image and graph levels using a contrastive based mechanism. Secondly, we present a dynamically adjusted hypergraph diffusion model, via a semi-explicit flow, to improve the predictive uncertainty. We demonstrate, through our experiments, that our framework is able to outperform current techniques for Alzheimer's disease diagnosis.

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