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Evaluating unsupervised contrastive learning framework for MRI sequences classification

2025-01-12 21:30:44
Yuli Wang, Kritika Iyer, Sep Farhand, Yoshihisa Shinagawa

Abstract

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.

Abstract (translated)

磁共振成像(MRI)序列的自动识别可以简化临床工作流程,通过减少放射科医生手动排序和识别序列的时间,从而加快患者的诊断和治疗计划。然而,MRI扫描参数缺乏标准化给自动化系统带来了挑战,并且使机器学习研究中数据集的生成和利用变得更加复杂。为了解决这个问题,我们提出了一种使用无监督对比深度学习框架进行MRI序列识别的系统。通过基于ResNet-18架构训练卷积神经网络,我们的系统将九种常见的MRI序列类型作为9类分类问题来进行分类。该网络使用内部数据集进行训练,并在包括BraTS、ADNI、融合放射病理前列腺数据集以及乳腺癌数据集(ACRIN)在内的多个公共数据集上进行了验证,这些数据集涵盖了不同的采集协议,并且仅需2D切片即可完成训练。我们的系统在这九种最常见的MRI序列类型中实现了超过95%的分类准确率。

URL

https://arxiv.org/abs/2501.06938

PDF

https://arxiv.org/pdf/2501.06938.pdf


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