Scalable, Axiomatic Explanations of Deep Alzheimer's Diagnosis from Heterogeneous Data
Artificial Intelligence in Medical Imaging (AI-Med), Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany（慕尼黑路德维希-马克西米利安大学儿童与青少年精神病学系医学成像人工智能（AI-Med））
Sebastian Pölsterl, Christina Aigner, Christian Wachinger
备注：
Accepted at 2021 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI)
Deep Neural Networks (DNNs) have an enormous potential to learn from complex biomedical data. In particular, DNNs have been used to seamlessly fuse heterogeneous information from neuroanatomy, genetics, biomarkers, and neuropsychological tests for highly accurate Alzheimer's disease diagnosis. On the other hand, their black-box nature is still a barrier for the adoption of such a system in the clinic, where interpretability is absolutely essential. We propose Shapley Value Explanation of Heterogeneous Neural Networks (SVEHNN) for explaining the Alzheimer's diagnosis made by a DNN from the 3D point cloud of the neuroanatomy and tabular biomarkers. Our explanations are based on the Shapley value, which is the unique method that satisfies all fundamental axioms for local explanations previously established in the literature. Thus, SVEHNN has many desirable characteristics that previous work on interpretability for medical decision making is lacking. To avoid the exponential time complexity of the Shapley value, we propose to transform a given DNN into a Lightweight Probabilistic Deep Network without re-training, thus achieving a complexity only quadratic in the number of features. In our experiments on synthetic and real data, we show that we can closely approximate the exact Shapley value with a dramatically reduced runtime and can reveal the hidden knowledge the network has learned from the data.
Bangladesh University of Engineering and Technology (BUET)（孟加拉国工程技术大学（BUET））；Georgia Institute of Technology（佐治亚理工学院）
Successful identification of blood vessel blockage is a crucial step for Alzheimer's disease diagnosis. These blocks can be identified from the spatial and time-depth variable Two-Photon Excitation Microscopy (TPEF) images of the brain blood vessels using machine learning methods. In this study, we propose several preprocessing schemes to improve the performance of these methods. Our method includes 3D-point cloud data extraction from image modality and their feature-space fusion to leverage complementary information inherent in different modalities. We also enforce the learned representation …
Ludwig-Maximilians-Universität（路德维希-马克西米利安大学）；Indiana University School of Medicine（印第安纳大学医学院）
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers. However, to improve our understanding of the disease, it is paramount to extract such knowledge from the learned model. In this paper, we propose a Deep Factorization Machine model that combines the ability of DNNs to learn complex relationships and the ease of interpretability of a linear model. The proposed model has three parts: (i) an embedding layer to deal with spar…
Univ. Bordeaux（波尔多大学）；CNRS（法国国家科学研究中心）；Bordeaux INP（波尔多国立高等物理、数学与信息技术学院）；Universitat Politècnia de València（瓦伦西亚理工大学）
Alzheimer's disease is the most common dementia leading to an irreversible neurodegenerative process. To date, subject revealed advanced brain structural alterations when the diagnosis is established. Therefore, an earlier diagnosis of this dementia is crucial although it is a challenging task. Recently, many studies have proposed biomarkers to perform early detection of Alzheimer's disease. Some of them have proposed methods based on inter-subject similarity while other approaches have investigated framework using intra-subject variability. In this work, we propose a novel framework combining…
In recent days, Convolutional Neural Networks (CNN) have demonstrated impressive performance in medical image analysis. However, there is a lack of clear understanding of why and how the Convolutional Neural Network performs so well for image analysis task. How CNN analyzes an image and discriminates among samples of different classes are usually considered as non-transparent. As a result, it becomes difficult to apply CNN based approaches in clinical procedures and automated disease diagnosis systems. In this paper, we consider this issue and work on visualizing and understanding the decision…
Lehigh University（理海大学）；University of Pittsburgh（匹兹堡大学）；The Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
The assessment of Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI) associated with brain changes remains a challenging task. Recent studies have demonstrated that combination of multi-modality imaging techniques can better reflect pathological characteristics and contribute to more accurate diagnosis of AD and MCI. In this paper, we propose a novel tensor-based multi-modality feature selection and regression method for diagnosis and biomarker identification of AD and MCI from normal controls. Specifically, we leverage the tensor structure to exploit high-level correlation informati…
National Institute of Mental Health and Neurosciences (NIMHANS)（国家精神卫生与神经科学研究所（NIMHANS））
Alzheimer's Disease (AD) is the most widespread neurodegenerative disease, affecting over 50 million people across the world. While its progression cannot be stopped, early and accurate diagnostic testing can drastically improve quality of life in patients. Currently, only qualitative means of testing are employed in the form of scoring performance on a battery of cognitive tests. The inherent disadvantage of this method is that the burden of an accurate diagnosis falls on the clinician's competence. Quantitative methods like MRI scan assessment are inaccurate at best,due to the elusive nature…
School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）；In-Chao Institute（因超研究所）
Alzheimer's disease (AD) is an irreversible neurode generative disease of the brain.The disease may causes memory loss, difficulty communicating and disorientation. For the diagnosis of Alzheimer's disease, a series of scales are often needed to evaluate the diagnosis clinically, which not only increases the workload of doctors, but also makes the results of diagnosis highly subjective. Therefore, for Alzheimer's disease, imaging means to find early diagnostic markers has become a top priority. In this paper, we propose a novel 3DMgNet architecture which is a unified framework of multigrid and…
Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. One popular choice for explaining deep image models is occluding regions of the image to isolate their influence on the prediction. However, existing methods for occluding patches of brain scans generate images outside the distribution to whi…
Leveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Huacong Qingjiao Information Technology (Beijing) Co., Ltd.（华聪青教信息技术（北京）有限公司）；Chongqing Technology and Business University（重庆工商大学）；University of Maine at Presque Isle（缅因大学普雷斯克艾尔分校）；San Francisco Bay University（旧金山湾大学）；Universitario Tecnológico Universitam；Northern Arizona University（北亚利桑那大学）
NVIDIA Corporation（英伟达公司）；Seoul National University（首尔大学）
Positron Emission Tomography (PET) is now regarded as the gold standard for the diagnosis of Alzheimer's Disease (AD). However, PET imaging can be prohibitive in terms of cost and planning, and is also among the imaging techniques with the highest dosage of radiation. Magnetic Resonance Imaging (MRI), in contrast, is more widely available and provides more flexibility when setting the desired image resolution. Unfortunately, the diagnosis of AD using MRI is difficult due to the very subtle physiological differences between healthy and AD subjects visible on MRI. As a result, many attempts have…
Indian Institute of Technology Indore（印度理工学院印多尔分校）
Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the significant causes of death in the elderly population. Many deep learning techniques have been proposed to diagnose AD using Magnetic Resonance Imaging (MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is challenging as the inter-slice information gets lost. To this end, we propose a novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D MRI scans that model both the intra-slice and inter-slice information. Biceph-Net has been experimentally shown to perform similar to other S…
Technische Hochschule Aschaffenburg（阿沙芬堡应用技术大学）；Universidade de São Paulo (USP)（圣保罗大学）
Mental disorders are among the leading causes of disability worldwide. The first step in treating these conditions is to obtain an accurate diagnosis, but the absence of established clinical tests makes this task challenging. Machine learning algorithms can provide a possible solution to this problem, as we describe in this work. We present a method for the automatic diagnosis of mental disorders based on the matrix of connections obtained from EEG time series and deep learning. We show that our approach can classify patients with Alzheimer's disease and schizophrenia with a high level of accu…
Isik University（伊希克大学）；Istanbul Technical University（伊斯坦布尔技术大学）
Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the curr…
University of Cambridge（剑桥大学）；Universite de Bordeaux（波尔多大学）
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-imagi…
AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease
School of Electronics and Information Engineering, Hebei University of Technology（河北工业大学电子与信息工程学院）；School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast（贝尔法斯特女王大学电子、电气工程与计算机科学学院）；School of Artificial Intelligence, Hebei University of Technology（河北工业大学人工智能学院）
Alzheimer's Disease (AD) is the most common neurodegenerative disorder with one of the most complex pathogeneses, making effective and clinically actionable decision support difficult. The objective of this study was to develop a novel multimodal deep learning framework to aid medical professionals in AD diagnosis. We present a Multimodal Alzheimer's Disease Diagnosis framework (MADDi) to accurately detect the presence of AD and mild cognitive impairment (MCI) from imaging, genetic, and clinical data. MADDi is novel in that we use cross-modal attention, which captures interactions between moda…
Lehigh University（里海大学）；University of Pennsylvania（宾夕法尼亚大学）
Identification of brain regions related to the specific neurological disorders are of great importance for biomarker and diagnostic studies. In this paper, we propose an interpretable Graph Convolutional Network (GCN) framework for the identification and classification of Alzheimer's disease (AD) using multi-modality brain imaging data. Specifically, we extended the Gradient Class Activation Mapping (Grad-CAM) technique to quantify the most discriminative features identified by GCN from brain connectivity patterns. We then utilized them to find signature regions of interest (ROIs) by detecting…
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
Faculty of Electrical and Computer Engineering, National Technical University of Athens（雅典国立技术大学电气与计算机工程学院）；Center for Biomedical Image Computing and Analytics, University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）
Texas Tech Neuroimaging Institute, Texas Tech University（德克萨斯理工大学神经影像研究所，德克萨斯理工大学）
Accurate diagnosis of Alzheimer's disease (AD) is both challenging and time consuming. With a systematic approach for early detection and diagnosis of AD, steps can be taken towards the treatment and prevention of the disease. This study explores the practical application of deep learning models for diagnosis of AD. Due to computational complexity, large training times and limited availability of labelled dataset, a 3D full brain CNN (convolutional neural network) is not commonly used, and researchers often prefer 2D CNN variants. In this study, full brain 3D version of well-known 2D CNNs were…
Introduction: Alzheimer's disease is a type of dementia in which early diagnosis plays a major rule in the quality of treatment. Among new works in the diagnosis of Alzheimer's disease, there are many of them analyzing the voice stream acoustically, syntactically or both. The mostly used tools to perform these analysis usually include machine learning techniques. Objective: Designing an automatic machine learning based diagnosis system will help in the procedure of early detection. Also, systems, using noninvasive data are preferable. Methods: We used are classification system based on spoken …
3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation
Vo Thien Nhan Institute of Engineering, Ho Chi Minh City University of Technology (HUTECH), Vietnam（越南胡志明市技术大学（HUTECH）工程学院）；Ho Bac Nam Faculty of Computer Science and Engineering, Ho Chi Minh City University of Technology (HCMUT), Vietnam National University Ho Chi Minh City (VNU-HCM), Vietnam（越南胡志明市技术大学（HCMUT）计算机科学与工程学院，越南胡志明市国家大学（VNU-HCM））
University of Science and Technology of China（中国科学技术大学）；The Ohio State University（俄亥俄州立大学）
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients'life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions rel…
Bridging Brain Connectomes and Clinical Reports for Early Alzheimer's Disease Diagnosis
The University of Texas at Arlington（德克萨斯大学阿灵顿分校）；Indiana University Indianapolis（印第安纳大学印第安纳波利斯分校）；University of Cambridge（剑桥大学）；University of Sheffield（谢菲尔德大学）；The University of Georgia（佐治亚大学）
Towards early diagnosis of Alzheimer's disease: Advances in immune-related blood biomarkers and computational modeling approaches
Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)（弗劳恩霍夫算法与科学计算研究所（SCAI））；University of Bonn（波恩大学）；Tel Aviv University（特拉维夫大学）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；University of Barcelona（巴塞罗那大学）；ImmunoBrain Checkpoint Ltd.（ImmunoBrain Checkpoint有限公司）；Sagol School of Neuroscience（萨戈尔神经科学学院）
Institute of Smart Systems and Artificial Intelligence, Nazarbayev University（纳扎尔巴耶夫大学智能系统与人工智能研究所）
Alzheimer's disease (AD) is a progressive brain disorder that causes memory and functional impairments. The advances in machine learning and publicly available medical datasets initiated multiple studies in AD diagnosis. In this work, we utilize a multi-modal deep learning approach in classifying normal cognition, mild cognitive impairment and AD classes on the basis of structural MRI and diffusion tensor imaging (DTI) scans from the OASIS-3 dataset. In addition to a conventional multi-modal network, we also present an input agnostic architecture that allows diagnosis with either sMRI or DTI s…
OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis
Guangxi Normal University（广西师范大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Capital Medical University（首都医科大学）；Guilin Medical University（桂林医学院）
We develop three efficient approaches for generating visual explanations from 3D convolutional neural networks (3D-CNNs) for Alzheimer's disease classification. One approach conducts sensitivity analysis on hierarchical 3D image segmentation, and the other two visualize network activations on a spatial map. Visual checks and a quantitative localization benchmark indicate that all approaches identify important brain parts for Alzheimer's disease diagnosis. Comparative analysis show that the sensitivity analysis based approach has difficulty handling loosely distributed cerebral cortex, and appr…
Imperial College London（帝国理工学院）；King’s College London（伦敦国王学院）
Pattern recognition methods using neuroimaging data for the diagnosis of Alzheimer's disease have been the subject of extensive research in recent years. In this paper, we use deep learning methods, and in particular sparse autoencoders and 3D convolutional neural networks, to build an algorithm that can predict the disease status of a patient, based on an MRI scan of the brain. We report on experiments using the ADNI data set involving 2,265 historical scans. We demonstrate that 3D convolutional neural networks outperform several other classifiers reported in the literature and produce state-…
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；University of Sheffield（谢菲尔德大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
University of A Coruna（拉科鲁尼亚大学）；Biomedical Research Institute of A Coruña (INIBIC)（拉科鲁尼亚生物医学研究所（INIBIC））；University Hospital Complex of A Coruna (CHUAC)（拉科鲁尼亚大学医院综合体（CHUAC））
Early detection is crucial to prevent the progression of Alzheimer's disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis of AD in the earliest and hardest to detect stages. The main objective of this work is to develop a system that automatically detects the presence of the disease in sagittal magnetic resonance images (MRI), which are not generally used. Sagittal MRIs from ADNI and OASIS data sets were employed. Experiments were conducted using Transfer Learning (TL) techniques in order to achieve more ac…
Attention-based Efficient Classification for 3D MRI Image of Alzheimer's Disease
Faculty of Computing, Harbin Institute of Technology（哈尔滨工业大学计算学部）；Harbin No.3 High School（哈尔滨市第三中学）；School of Life Science and Technology, Harbin Institute of Technology（哈尔滨工业大学生命科学与技术学院）；College of Public Health, George Mason University（乔治梅森大学公共卫生学院）
A Generative Imputation Method for Multimodal Alzheimer's Disease Diagnosis
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA（电气与计算机工程学院，佐治亚理工学院，亚特兰大，GA，USA）；Courtesy Faculty Appointment, College of Pharmacy, University of Florida, Gainesville, FL, USA（佛罗里达大学药学院荣誉教职）
Enabling Few-Shot Alzheimer's Disease Diagnosis on Biomarker Data with Tabular LLMs
University of Pennsylvania（宾夕法尼亚大学）；United States Naval Academy（美国海军学院）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Cedars Sinai Medical Center（西好莱坞 Cedars Sinai 医院）
There is a need for automatic diagnosis of certain diseases from medical images that could help medical practitioners for further assessment towards treating the illness. Alzheimers disease is a good example of a disease that is often misdiagnosed. Alzheimers disease (Hear after referred to as AD), is caused by atrophy of certain brain regions and by brain cell death and is the leading cause of dementia and memory loss [1]. MRI scans reveal this information but atrophied regions are different for different individuals which makes the diagnosis a bit more trickier and often gets misdiagnosed [1…
