用无监督对比学习自动识别9类MRI序列，准确率超95%
Evaluating unsupervised contrastive learning framework for MRI sequences classification
- 基于ResNet-18的无监督对比学习框架，仅需2D切片训练
- 在9类MRI序列上实现超过95%的分类准确率
- 适用于多协议、非标准化数据集，适合临床部署
自动识别磁共振成像（MRI）序列可减少放射科医生手动排序与识别的时间，从而加快诊断与治疗规划。然而，MRI扫描参数缺乏标准化，给自动化系统带来挑战，并阻碍了机器学习研究中数据集的构建与使用。为此，我们提出一种基于无监督对比深度学习框架的MRI序列识别系统。通过基于ResNet-18架构的卷积神经网络，将九种常见MRI序列作为九分类问题进行识别。模型在内部数据集上训练，并在多个公开数据集（包括BraTS、ADNI、Fused Radiology-Pathology Prostate Dataset、ACRIN乳腺癌数据集）上验证，涵盖多种采集协议，且仅需2D切片进行训练。系统在九类常见MRI序列上的分类准确率超过0.95。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。