标题： 评估无监督对比学习框架在MRI序列分类中的应用 显示英文标题

标题： Evaluating unsupervised contrastive learning framework for MRI sequences classification

摘要： 磁共振成像（MRI）序列的自动识别可以通过减少放射科医生手动分类和识别序列所花费的时间，从而简化临床工作流程，使患者能够更快地进行诊断和治疗计划。 然而，MRI扫描参数缺乏标准化，给自动化系统带来了挑战，并使得机器学习研究的数据集生成和利用变得更加复杂。 为了解决这个问题，我们提出了一种基于无监督对比深度学习框架的MRI序列识别系统。 通过在ResNet-18架构基础上训练卷积神经网络，我们的系统将九种常见的MRI序列类型作为九类分类问题进行分类。 该网络使用内部数据集进行训练，并在多个公开数据集上进行验证，包括BraTS、ADNI、Fused Radiology-Pathology Prostate Dataset、乳腺癌数据集（ACRIN）等，涵盖了多种采集协议，并且仅需2D切片进行训练。 我们的系统在九种最常见的MRI序列类型上的分类准确率超过0.95。 显示英文摘要

摘要： 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.