Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation
- URL: http://arxiv.org/abs/2407.08546v1
- Date: Thu, 11 Jul 2024 14:30:49 GMT
- Title: Quantitative Evaluation of the Saliency Map for Alzheimer's Disease Classifier with Anatomical Segmentation
- Authors: Yihan Zhang, Xuanshuo Zhang, Wei Wu, Haohan Wang,
- Abstract summary: Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD)
In this paper, we utilize the anatomical segmentation to allocate saliency values into different brain regions.
By plotting the distributions of saliency maps corresponding to AD and NC (Normal Control), we can gain a comprehensive view of the model's decisions process.
- Score: 19.678873653172513
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Saliency maps have been widely used to interpret deep learning classifiers for Alzheimer's disease (AD). However, since AD is heterogeneous and has multiple subtypes, the pathological mechanism of AD remains not fully understood and may vary from patient to patient. Due to the lack of such understanding, it is difficult to comprehensively and effectively assess the saliency map of AD classifier. In this paper, we utilize the anatomical segmentation to allocate saliency values into different brain regions. By plotting the distributions of saliency maps corresponding to AD and NC (Normal Control), we can gain a comprehensive view of the model's decisions process. In order to leverage the fact that the brain volume shrinkage happens in AD patients during disease progression, we define a new evaluation metric, brain volume change score (VCS), by computing the average Pearson correlation of the brain volume changes and the saliency values of a model in different brain regions for each patient. Thus, the VCS metric can help us gain some knowledge of how saliency maps resulting from different models relate to the changes of the volumes across different regions in the whole brain. We trained candidate models on the ADNI dataset and tested on three different datasets. Our results indicate: (i) models with higher VCSs tend to demonstrate saliency maps with more details relevant to the AD pathology, (ii) using gradient-based adversarial training strategies such as FGSM and stochastic masking can improve the VCSs of the models.
Related papers
- Graph Theory and GNNs to Unravel the Topographical Organization of Brain Lesions in Variants of Alzheimer's Disease Progression [0.0]
We proposed and evaluated a graph-based framework to assess variations in Alzheimer's disease (AD) neuropathologies.
Our framework focuses on classic (cAD) and rapid (rpAD) progression forms.
Results suggest a unique neuropathological network organization for each AD variant.
arXiv Detail & Related papers (2024-03-01T16:16:51Z) - Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRI [55.78588835407174]
We propose a method that reformulates the generation task of diffusion models as a patch-based estimation of healthy brain anatomy.
We evaluate our approach on data of tumors and multiple sclerosis lesions and demonstrate a relative improvement of 25.1% compared to existing baselines.
arXiv Detail & Related papers (2023-03-07T09:40:22Z) - Normative Modeling via Conditional Variational Autoencoder and
Adversarial Learning to Identify Brain Dysfunction in Alzheimer's Disease [10.302206705998563]
We propose a novel normative modeling method by combining conditional variational autoencoder with adversarial learning (ACVAE) to identify brain dysfunction in Alzheimer's Disease (AD)
Our experiments on OASIS-3 database show that the deviation maps generated by our model exhibit higher sensitivity to AD compared to other deep normative models.
arXiv Detail & Related papers (2022-11-13T07:36:30Z) - XADLiME: eXplainable Alzheimer's Disease Likelihood Map Estimation via
Clinically-guided Prototype Learning [3.286378299443229]
We propose a novel deep-learning approach through XADLiME for AD progression modeling over 3D sMRIs.
Specifically, we establish a set of topologically-aware prototypes onto the clusters of latent clinical features, uncovering an AD spectrum manifold.
We then measure the similarities between latent clinical features and well-established prototypes, estimating a "pseudo" likelihood map.
arXiv Detail & Related papers (2022-07-27T00:25:55Z) - Building Brains: Subvolume Recombination for Data Augmentation in Large
Vessel Occlusion Detection [56.67577446132946]
A large training data set is required for a standard deep learning-based model to learn this strategy from data.
We propose an augmentation method that generates artificial training samples by recombining vessel tree segmentations of the hemispheres from different patients.
In line with the augmentation scheme, we use a 3D-DenseNet fed with task-specific input, fostering a side-by-side comparison between the hemispheres.
arXiv Detail & Related papers (2022-05-05T10:31:57Z) - Deep Joint Learning of Pathological Region Localization and Alzheimer's
Disease Diagnosis [4.5484714814315685]
BrainBagNet is a framework for jointly learning pathological region localization and Alzheimer's disease diagnosis.
The proposed method represents the patch-level response from whole-brain MRI scans and discriminative brain-region from position information.
In five-fold cross-validation, the classification performance of the proposed method outperformed that of the state-of-the-art methods in both AD diagnosis and mild cognitive impairment prediction tasks.
arXiv Detail & Related papers (2021-08-10T10:06:54Z) - An explainable two-dimensional single model deep learning approach for
Alzheimer's disease diagnosis and brain atrophy localization [3.9281410693767036]
We propose an end-to-end deep learning approach for automated diagnosis of Alzheimer's disease (AD) and localization of important brain regions related to the disease from sMRI data.
Our approach has been evaluated on two publicly accessible datasets for two classification tasks of AD vs. cognitively normal (CN) and progressive MCI (pMCI) vs. stable MCI (sMCI)
The experimental results indicate that our approach outperforms the state-of-the-art approaches, including those using multi-model and 3D CNN methods.
arXiv Detail & Related papers (2021-07-28T07:19:00Z) - Relational Subsets Knowledge Distillation for Long-tailed Retinal
Diseases Recognition [65.77962788209103]
We propose class subset learning by dividing the long-tailed data into multiple class subsets according to prior knowledge.
It enforces the model to focus on learning the subset-specific knowledge.
The proposed framework proved to be effective for the long-tailed retinal diseases recognition task.
arXiv Detail & Related papers (2021-04-22T13:39:33Z) - 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) - Adversarial Sample Enhanced Domain Adaptation: A Case Study on
Predictive Modeling with Electronic Health Records [57.75125067744978]
We propose a data augmentation method to facilitate domain adaptation.
adversarially generated samples are used during domain adaptation.
Results confirm the effectiveness of our method and the generality on different tasks.
arXiv Detail & Related papers (2021-01-13T03:20:20Z) - 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.