Evaluating unsupervised contrastive learning framework for MRI sequences classification
The automatic identification of Magnetic Resonance Imaging (MRI) sequences can streamline clinical workflows by reducing the time radiologists spend manually sorting and identifying sequences, thereby enabling faster diagnosis and treatment planning for patients. However, the lack of standardization in the parameters of MRI scans poses challenges for automated systems and complicates the generation and utilization of datasets for machine learning research. To address this issue, we propose a system for MRI sequence identification using an unsupervised contrastive deep learning framework. By training a convolutional neural network based on the ResNet-18 architecture, our system classifies nine common MRI sequence types as a 9-class classification problem. The network was trained using an in-house internal dataset and validated on several public datasets, including BraTS, ADNI, Fused Radiology-Pathology Prostate Dataset, the Breast Cancer Dataset (ACRIN), among others, encompassing diverse acquisition protocols and requiring only 2D slices for training. Our system achieves a classification accuracy of over 0.95 across the nine most common MRI sequence types.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningSimilar Papers 제목 키워드 기반
CGRclust: Chaos Game Representation for Twin Contrastive Clustering of Unlabelled DNA Sequences
This study proposes CGRclust, a novel combination of unsupervised twin contrastive clustering of Chaos Game Representations (CGR) of DNA sequences, with convolutional neural networks (CNNs). To the best of our knowledge,…
ClusteringContrastive Learningimage-classificationImage ClassificationUnsupervised Graph Poisoning Attack via Contrastive Loss Back-propagation
Graph contrastive learning is the state-of-the-art unsupervised graph representation learning framework and has shown comparable performance with supervised approaches. However, evaluating whether the graph contrastive l…
Adversarial AttackContrastive LearningGraph Representation LearningLink Prediction+2Semi-Supervised End-To-End Contrastive Learning For Time Series Classification
Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis. Unsupervised contrastive learning has garnered significant interest in learning effective represent…
ClassificationContrastive LearningTime SeriesTime Series Classification+1SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification
Recent advances in skeleton-based person re-identification (re-ID) obtain impressive performance via either hand-crafted skeleton descriptors or skeleton representation learning with deep learning paradigms. However, the…
Contrastive LearningPerson Re-IdentificationRepresentation LearningUnsupervised Person Re-IdentificationExploring Denoised Cross-Video Contrast for Weakly-Supervised Temporal Action Localization
Weakly-supervised temporal action localization aims to localize actions in untrimmed videos with only video-level labels. Most existing methods address this problem with a "localization-by-classification" pipeline th…
Action LocalizationContrastive LearningDenoisingPseudo Label+3