Papers MRI classification
“MRI classification” 태그가 달린 논문 19편 · 필터 해제
A Foundation Model Framework for Multi-View MRI Classification of Extramural Vascular Invasion and Mesorectal Fascia Invasion in Rectal Cancer
Background: Accurate MRI-based identification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is pivotal for risk-stratified management of rectal cancer, yet visual assessment is subjective and…
DiagnosticMRI classification3D Brain MRI Classification for Alzheimer Diagnosis Using CNN with Data Augmentation
A three-dimensional convolutional neural network was developed to classify T1-weighted brain MRI scans as healthy or Alzheimer. The network comprises 3D convolution, pooling, batch normalization, dense ReLU layers, and a…
Data AugmentationMRI classificationSensitivitySpecificityMedCAM-OsteoCls: Medical Context Aware Multimodal Classification of Knee Osteoarthritis
Knee Osteoarthritis (KOA) is a degenerative musculoskeletal joint disorder that significantly impacts middle-aged and elderly individuals. Although X-rays and MRIs are clinically used to identify such disorders, combinin…
AnatomyClassificationDiagnosticMRI classification+1Analyzing the Effect of $k$-Space Features in MRI Classification Models
The integration of Artificial Intelligence (AI) in medical diagnostics is often hindered by model opacity, where high-accuracy systems function as "black boxes" without transparent reasoning. This limitation is critical …
DiagnosticMRI classificationMulti-SIGATnet: A multimodal schizophrenia MRI classification algorithm using sparse interaction mechanisms and graph attention networks
Schizophrenia is a serious psychiatric disorder. Its pathogenesis is not completely clear, making it difficult to treat patients precisely. Because of the complicated non-Euclidean network structure of the human brain, l…
Graph AttentionMRI classificationSparse LearningLeveraging Deep Learning and Xception Architecture for High-Accuracy MRI Classification in Alzheimer Diagnosis
Exploring the application of deep learning technologies in the field of medical diagnostics, Magnetic Resonance Imaging (MRI) provides a unique perspective for observing and diagnosing complex neurodegenerative diseases …
Deep Learningimage-classificationImage ClassificationMRI classificationEnhancing Automated and Early Detection of Alzheimer's Disease Using Out-Of-Distribution Detection
More than 10.7% of people aged 65 and older are affected by Alzheimer's disease. Early diagnosis and treatment are crucial as most Alzheimer's patients are unaware of having it until the effects become detrimental. AI ha…
MRI classificationOut-of-Distribution DetectionOut of Distribution (OOD) DetectionBenchmark data to study the influence of pre-training on explanation performance in MR image classification
Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the…
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)image-classificationImage Classification+2Video4MRI: An Empirical Study on Brain Magnetic Resonance Image Analytics with CNN-based Video Classification Frameworks
To address the problem of medical image recognition, computer vision techniques like convolutional neural networks (CNN) are frequently used. Recently, 3D CNN-based models dominate the field of magnetic resonance image (…
ClassificationData AugmentationMRI classificationVideo Classification+2Brain Tumor MRI Classification using a Novel Deep Residual and Regional CNN
Brain tumor classification is crucial for clinical analysis and an effective treatment plan to cure patients. Deep learning models help radiologists to accurately and efficiently analyze tumors without manual interventio…
Brain Tumor ClassificationMRI classificationAutomatic Classification of Alzheimer's Disease using brain MRI data and deep Convolutional Neural Networks
Alzheimer's disease (AD) is one of the most common public health issues the world is facing today. This disease has a high prevalence primarily in the elderly accompanying memory loss and cognitive decline. AD detection …
Binary ClassificationDeep LearningImage SegmentationMedical Image Analysis+2Effect of data leakage in brain MRI classification using 2D convolutional neural networks
In recent years, 2D convolutional neural networks (CNNs) have been extensively used to diagnose neurological diseases from magnetic resonance imaging (MRI) data due to their potential to discern subtle and intricate patt…
MRI classificationContrastive Learning with Continuous Proxy Meta-Data for 3D MRI Classification
Traditional supervised learning with deep neural networks requires a tremendous amount of labelled data to converge to a good solution. For 3D medical images, it is often impractical to build a large homogeneous annotate…
Alzheimer's DetectionContrastive LearningMRI classificationAccuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort
BACKGROUND:Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic …
DiagnosticGeneral ClassificationMRI classificationEnsemble Deep Learning on Large, Mixed-Site fMRI Datasets in Autism and Other Tasks
Deep learning models for MRI classification face two recurring problems: they are typically limited by low sample size, and are abstracted by their own complexity (the "black box problem"). In this paper, we train a conv…
MRI classification3D Deformable Convolutions for MRI classification
Deep learning convolutional neural networks have proved to be a powerful tool for MRI analysis. In current work, we explore the potential of the deformable convolutional deep neural network layers for MRI data classifica…
ClassificationGeneral ClassificationMRI classificationGlobal and Local Interpretability for Cardiac MRI Classification
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice. In this…
ClassificationGeneral ClassificationMRI classificationTemporal SequencesAlzheimer's Disease Brain MRI Classification: Challenges and Insights
In recent years, many papers have reported state-of-the-art performance on Alzheimer's Disease classification with MRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset using convolutional neural …
ClassificationGeneral ClassificationMRI classificationResidual and Plain Convolutional Neural Networks for 3D Brain MRI Classification
In the recent years there have been a number of studies that applied deep learning algorithms to neuroimaging data. Pipelines used in those studies mostly require multiple processing steps for feature extraction, althoug…
ClassificationDeep LearningGeneral Classificationimage-classification+2