Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
Erasmus MC（伊拉斯谟医学中心）；Maastricht University Medical Center（马斯特里赫特大学医学中心）；University Medical Center Groningen（格罗宁根大学医学中心）；University Medical Center Utrecht（乌得勒支大学医学中心）；Radboud University Medical Center（拉德堡德大学医学中心）；Leiden University Medical Center（莱顿大学医学中心）；Leiden University（莱顿大学）
Esther E. Bron, Stefan Klein, Janne M. Papma, Lize C. Jiskoot, Vikram Venkatraghavan, Jara Linders, Pauline Aalten, Peter Paul De Deyn, Geert Jan Biessels, Jurg… 展开作者
Esther E. Bron, Stefan Klein, Janne M. Papma, Lize C. Jiskoot, Vikram Venkatraghavan, Jara Linders, Pauline Aalten, Peter Paul De Deyn, Geert Jan Biessels, Jurgen A. H. R. Claassen, Huub A. M. Middelkoop, Marion Smits, Wiro J. Niessen, John C. van Swieten, Wiesje M. van der Flier, Inez H. G. B. Ramakers, Aad van der Lugt
This work validates the generalizability of MRI-based classification of Alzheimer's disease (AD) patients and controls (CN) to an external data set and to the task of prediction of conversion to AD in individuals with mild cognitive impairment (MCI). We used a conventional support vector machine (SVM) and a deep convolutional neural network (CNN) approach based on structural MRI scans that underwent either minimal pre-processing or more extensive pre-processing into modulated gray matter (GM) maps. Classifiers were optimized and evaluated using cross-validation in the ADNI (334 AD, 520 CN). Trained classifiers were subsequently applied to predict conversion to AD in ADNI MCI patients (231 converters, 628 non-converters) and in the independent Health-RI Parelsnoer data set. From this multi-center study representing a tertiary memory clinic population, we included 199 AD patients, 139 participants with subjective cognitive decline, 48 MCI patients converting to dementia, and 91 MCI patients who did not convert to dementia. AD-CN classification based on modulated GM maps resulted in a similar AUC for SVM (0.940) and CNN (0.933). Application to conversion prediction in MCI yielded significantly higher performance for SVM (0.756) than for CNN (0.742). In external validation, performance was slightly decreased. For AD-CN, it again gave similar AUCs for SVM (0.896) and CNN (0.876). For prediction in MCI, performances decreased for both SVM (0.665) and CNN (0.702). Both with SVM and CNN, classification based on modulated GM maps significantly outperformed classification based on minimally processed images. Deep and conventional classifiers performed equally well for AD classification and their performance decreased only slightly when applied to the external cohort. We expect that this work on external validation contributes towards translation of machine learning to clinical practice.
Manchester Metropolitan University（曼彻斯特城市大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal form mild cognitive impairment (MCI) based on structure Magnetic Resonance Imaging (sMRI) has provided a cost-effective and objective way for early prevention and treatment of disease progression, leading to improved patient care. In this work, we have proposed a novel computer-aided approach for early diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from the existing approaches, the novelty of our approach is thre…
Perelman School of Medicine, University of Pennsylvania（宾夕法尼亚大学佩雷尔曼医学院）
Multi-modal biological, imaging, and neuropsychological markers have demonstrated promising performance for distinguishing Alzheimer's disease (AD) patients from cognitively normal elders. However, it remains difficult to early predict when and which mild cognitive impairment (MCI) individuals will convert to AD dementia. Informed by pattern classification studies which have demonstrated that pattern classifiers built on longitudinal data could achieve better classification performance than those built on cross-sectional data, we develop a deep learning model based on recurrent neural networks…
Increasing effort in brain image analysis has been dedicated to early diagnosis of Alzheimer's disease (AD) based on neuroimaging data. Most existing studies have been focusing on binary classification problems, e.g., distinguishing AD patients from normal control (NC) elderly or mild cognitive impairment (MCI) individuals from NC elderly. However, identifying individuals with AD and MCI, especially MCI individuals who will convert to AD (progressive MCI, pMCI), in a single setting, is needed to achieve the goal of early diagnosis of AD. In this paper, we propose a deep ordinal ranking model f…
Early Alzheimer's Disease Detection from Retinal OCT Images: A UK Biobank Study
Department of Computer Engineering Isik University Istanbul, Turkey（计算机工程系伊斯肯大学）；Department of Artificial Intelligence and Data Engineering（人工智能与数据工程系）；Data Engineering Istanbul Technical University Istanbul, Turkey（数据工程系伊斯坦布尔技术大学）
CohortFinder: an open-source tool for data-driven partitioning of biomedical image cohorts to yield robust machine learning models
Emory University（埃默里大学）；Georgia Institute of Technology（佐治亚理工学院）；Case Western Reserve University（凯斯西储大学）；Duke University（杜克大学）；University of Michigan（密歇根大学）；University Hospital of Zurich（苏黎世大学医院）；University of Zurich（苏黎世大学）；Atlanta Veterans Administration Medical Center（亚特兰大退伍军人管理医疗中心）；University Hospital of Geneva（日内瓦大学医院）
Incorporating Anatomical Awareness for Enhanced Generalizability and Progression Prediction in Deep Learning-Based Radiographic Sacroiliitis Detection
Department of Radiology, Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin（柏林查理医院放射科，弗赖堡大学柏林分校和洪堡大学成员）；Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School（阿提诺拉A.马丁努斯生物医学成像中心，麻省总医院和哈佛医学院）；Department of Gastroenterology, Infectious Diseases and Rheumatology (incl. nutrition medicine), Charité - Universitätsmedizin Berlin corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin（胃肠病学、传染病学和风湿病学（含营养医学）部门，柏林查理医院，弗赖堡大学柏林分校和洪堡大学成员）；Department of Diagnostic and Interventional Radiology, Faculty of Medicine, Technical University of Munich（诊断和介入放射科，医学院，慕尼黑技术大学）
Vanderbilt University（范德堡大学）；Sigma Technologies（12西格玛科技公司）；Cleveland Clinic（克利夫兰诊所）；Vanderbilt University Medical Center（范德堡大学医学中心）
Tissue window filtering has been widely used in deep learning for computed tomography (CT) image analyses to improve training performance (e.g., soft tissue windows for abdominal CT). However, the effectiveness of tissue window normalization is questionable since the generalizability of the trained model might be further harmed, especially when such models are applied to new cohorts with different CT reconstruction kernels, contrast mechanisms, dynamic variations in the acquisition, and physiological changes. We evaluate the effectiveness of both with and without using soft tissue window norma…
PANDA - Prototype-Anchored Alignment for Partially Unpaired Multimodal Learning, with Applications to Alzheimers MRI and TCGA Pathology
Friedrich-Alexander-Universität（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）；Klinikum Nürnberg, Paracelsus Medical University（帕拉塞尔苏斯医科大学纽伦堡医院）；Ludwig-Maximilians-Universität München（慕尼黑大学）；National Institute of Mental Health and Neurosciences (NIMHANS)（国家精神卫生与神经科学研究院）
PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis
Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California（斯蒂文斯神经影像与信息学研究所，凯克医学院，南加州大学）；Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California（明希德电气与计算机工程系，维特比工程学院，南加州大学）；Alfred E. Mann Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California（阿尔弗雷德·E·曼生物医学工程系，维特比工程学院，南加州大学）
A Generalizable Feature Extractor for Alzheimer's-Related Brain MRI Tasks
McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University（麦吉尔大学蒙特利尔神经学研究所麦康奈尔脑成像中心）；Department of Biomedical Engineering, McGill University（麦吉尔大学生物医学工程系）
Modality Contribution Score - A Per-Patient Framework for Quantifying the Relative Diagnostic Contribution of Structural MRI and Amyloid PET in Alzheimer's Disease
Taipei Medical University（台北医学大学）；University of Memphis（孟菲斯大学）；Chitkara University（奇特卡拉大学）；University College London（伦敦大学学院）；UPES（石油与能源研究大学）；University of Eastern Finland（东芬兰大学）；CNRS（法国国家科学研究中心）；University Bordeaux（波尔多大学）；Bordeaux INP（波尔多国立理工学院）
Three-dimensional end-to-end deep learning for brain MRI analysis
Technical University Dresden（德累斯顿工业大学）；University Hospital and Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院及医学院）；National Center for Tumor Diseases Dresden（德累斯顿国家肿瘤疾病中心）；University Hospital Heidelberg（海德堡大学医院）；National Center for Tumor Diseases（国家肿瘤疾病中心）；University Hospital Aachen（亚琛大学医院）；Carl Gustav Carus University Hospital, Technische Universität Dresden（德累斯顿工业大学卡尔·古斯塔夫·卡鲁斯大学医院）
Cyclic 2.5D Perceptual Loss for Cross-Modal 3D Medical Image Synthesis: T1w MRI to Tau PET
Department of Artificial Intelligence, Hanyang University, Seoul, South Korea（人工智能系，翰阳大学，首尔，韩国）；Department of Electronic Engineering, Hanyang University, Seoul, South Korea（电子工程系，翰阳大学，首尔，韩国）；Division of Computer Engineering, Hankuk University of Foreign Studies, Yongin, South Korea（计算机工程系，韩国外语大学， Yongin，韩国）；Division of AI Data Convergence, Hankuk University of Foreign Studies, Yongin, South Korea（AI数据融合系，韩国外语大学， Yongin，韩国）；Division of Language & AI, Hankuk University of Foreign Studies, Seoul, South Korea（语言与AI系，韩国外语大学，首尔，韩国）
OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation
NeuroBridge: Bridging Multi-Task MRI Knowledge for Neurodegenerative Disease Diagnosis
Department of Mechanical Engineering, Boston University（波士顿大学机械工程系）；The Photonics Center, Boston University（波士顿大学光子中心）；Rafik B. Hariri Institute for Computing and Computational Science & Engineering, Boston University（波士顿大学拉菲克·B·哈里里计算与计算科学与工程研究所）；Department of Radiology, Boston University Chobanian & Avedisian School of Medicine（波士顿大学切博尼亚与阿维迪亚安医学学院放射科）；Department of Radiology, Boston Medical Center（波士顿医学中心放射科）；Department of Electrical and Computer Engineering, Boston University（波士顿大学电气与计算机工程系）；Department of Biomedical Engineering, Boston University（波士顿大学生物医学工程系）；Division of Materials Science and Engineering, Boston University（波士顿大学材料科学与工程系）
Stress-testing cross-cancer generalizability of 3D nnU-Net for PET-CT tumor segmentation: multi-cohort evaluation with novel oesophageal and lung cancer datasets
QIMR Berghofer Medical Research Institute（昆士兰医学研究 institute）；Australian Institute for Bioengineering and Nanotechnology（澳大利亚生物工程与纳米技术研究所）；The University of Queensland（昆士兰大学）；Frazer Institute（弗拉泽研究所）；Princess Alexandra Hospital（亚历山大公主医院）；Faculty of Health, Medicine and Behavioural Sciences（健康、医学与行为科学学院）；School of Biomedical Sciences（生物医学科学学院）；Uppsala University（乌普萨拉大学）；Rohtak Nuclear Medcare Imaging Therapy and Research Center（罗塔克核医学成像治疗与研究中心）
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Department of Biomedical Engineering, Linköping University, Sweden（生物医学工程系，利厄普大学）；Center for Medical Image Science and Visualization, Linköping University, Sweden（医学影像科学与可视化中心，利厄普大学）；Crown Princess Victoria Children’s Hospital and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（维多利亚公主儿童医院和健康、医学与关怀科学系，利厄普大学）；Department of Radiology and Department of Health, Medicine and Caring Sciences, Linköping University, Sweden（放射科和健康、医学与关怀科学系，利厄普大学）；Department of Oncology-Pathology, Karolinska Institutet, Solna, Sweden（肿瘤病理学系，卡罗林斯卡研究所，索纳）；Department of Clinical Pathology and Cancer Diagnostics, Karolinska University Hospital, Sweden（临床病理学和癌症诊断系，卡罗林斯卡大学医院）；Department of Radiation Physics and Department of Medical and Health Sciences, Linköping University, Sweden（放射物理系和医学与健康科学系，利厄普大学）
University of Arizona（亚利桑那大学）；Mallinckrodt Institute of Radiology, Washington University（华盛顿大学马林克罗放射学研究所）；University of Utah（犹他大学）
The relationship between cognition and white matter hyperintensities (WMH) volumes often depends on the accuracy of the lesion segmentation algorithm used. As such, accurate detection and quantification of WMH is of great interest. Here, we use a deep learning-based WMH segmentation algorithm, StackGen-Net, to detect and quantify WMH on 3D FLAIR volumes from ADNI. We used a subset of subjects (n=20) and obtained manual WMH segmentations by an experienced neuro-radiologist to demonstrate the accuracy of our algorithm. On a larger cohort of subjects (n=290), we observed that larger WMH volumes c…
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD). Using a simple abstraction technique, we converted regional cortical and subcortical volume differences over two time points for each study subject into a graph. We then obtained substructures of interest using a graph decomposition algorithm in order to extract pivotal nodes via multi-view feature selection. Intensive experiments using robust classification frameworks were conducted to evaluate the performance of using the brain substructures obtai…
Alzheimer's disease (AD) is known as one of the major causes of dementia and is characterized by slow progression over several years, with no treatments or available medicines. In this regard, there have been efforts to identify the risk of developing AD in its earliest time. While many of the previous works considered cross-sectional analysis, more recent studies have focused on the diagnosis and prognosis of AD with longitudinal or time series data in a way of disease progression modeling (DPM). Under the same problem settings, in this work, we propose a novel computational framework that ca…
An Attention-Based Framework for Alzheimers Disease Classification Using Resting-State fMRI
School of Medical Science and Technology（医学科学与技术学院）；Dr B C Roy Multi Speciality Medical Research Centre（B C Roy多专科医学研究中心）；Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Indian Institute of Technology Gandhinagar（印度理工学院甘地纳格尔分校）
The TADPOLE Challenge compares the performance of algorithms at predicting the future evolution of individuals at risk of Alzheimer's disease. TADPOLE Challenge participants train their models and algorithms on historical data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) study. Participants are then required to make forecasts of three key outcomes for ADNI-3 rollover participants: clinical diagnosis, ADAS-Cog 13, and total volume of the ventricles -- which are then compared with future measurements. Strong points of the challenge are that the test data did not exist at the time …
University College London（伦敦大学学院）；Indiana University（印第安纳大学）；Panakeia Technologies（Panakeia 科技公司）
Biomechanical modeling of tissue deformation can be used to simulate different scenarios of longitudinal brain evolution. In this work,we present a deep learning framework for hyper-elastic strain modelling of brain atrophy, during healthy ageing and in Alzheimer's Disease. The framework directly models the effects of age, disease status, and scan interval to regress regional patterns of atrophy, from which a strain-based model estimates deformations. This model is trained and validated using 3D structural magnetic resonance imaging data from the ADNI cohort. Results show that the framework ca…
Alzheimer's Disease is the most common cause of dementia. Accurate diagnosis and prognosis of this disease are essential to design an appropriate treatment plan, increasing the life expectancy of the patient. Intense research has been conducted on the use of machine learning to identify Alzheimer's Disease from neuroimaging data, such as structural magnetic resonance imaging. In recent years, advances of deep learning in computer vision suggest a new research direction for this problem. Current deep learning-based approaches in this field, however, have a number of drawbacks, including the int…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
Since the outbreak of the COVID-19 pandemic, worldwide research efforts have focused on using artificial intelligence (AI) technologies on various medical data of COVID-19-positive patients in order to identify or classify various aspects of the disease, with promising reported results. However, concerns have been raised over their generalizability, given the heterogeneous factors in training datasets. This study aims to examine the severity of this problem by evaluating deep learning (DL) classification models trained to identify COVID-19-positive patients on 3D computed tomography (CT) datas…
Case Western Reserve University（凯斯西储大学）；Louis Stokes Cleveland VA Medical Center（路易斯·斯托克斯克利夫兰VA医疗中心）
We sought to develop a quantitative tool to quickly determine relative differences in MRI volumes both within and between large MR imaging cohorts (such as available in The Cancer Imaging Archive (TCIA)), in order to help determine the generalizability of radiomics and machine learning schemes to unseen datasets. The tool is intended to help quantify presence of (a) site- or scanner-specific variations in image resolution, field-of-view, or image contrast, or (b) imaging artifacts such as noise, motion, inhomogeneity, ringing, or aliasing; which can adversely affect relative image quality betw…
The Hong Kong University of Science and Technology（香港科技大学）
There is a rising need for computational models that can complementarily leverage data of different modalities while investigating associations between subjects for population-based disease analysis. Despite the success of convolutional neural networks in representation learning for imaging data, it is still a very challenging task. In this paper, we propose a generalizable framework that can automatically integrate imaging data with non-imaging data in populations for uncertainty-aware disease prediction. At its core is a learnable adaptive population graph with variational edges, which we ma…
