Abstract
Multi-parametric magnetic resonance imaging (mpMRI) exams have various series types acquired with different imaging protocols. The DICOM headers of these series often have incorrect information due to the sheer diversity of protocols and occasional technologist errors. To address this, we present a deep learning-based classification model to classify 8 different body mpMRI series types so that radiologists read the exams efficiently. Using mpMRI data from various institutions, multiple deep learning-based classifiers of ResNet, EfficientNet, and DenseNet are trained to classify 8 different MRI series, and their performance is compared. Then, the best-performing classifier is identified, and its classification capability under the setting of different training data quantities is studied. Also, the model is evaluated on the out-of-training-distribution datasets. Moreover, the model is trained using mpMRI exams obtained from different scanners in two training strategies, and its performance is tested. Experimental results show that the DenseNet-121 model achieves the highest F1-score and accuracy of 0.966 and 0.972 over the other classification models with p-value$<$0.05. The model shows greater than 0.95 accuracy when trained with over 729 studies of the training data, whose performance improves as the training data quantities grew larger. On the external data with the DLDS and CPTAC-UCEC datasets, the model yields 0.872 and 0.810 accuracy for each. These results indicate that in both the internal and external datasets, the DenseNet-121 model attains high accuracy for the task of classifying 8 body MRI series types.
Abstract (translated)
多参数磁共振成像（mpMRI）检查包含多种不同成像协议获取的序列类型。由于各种不同的协议以及技术员偶尔的操作错误，这些序列的DICOM头部信息常常出现不准确的情况。为此，我们提出了一种基于深度学习的分类模型，用于对8种不同的身体mpMRI序列类型进行分类，以提高放射科医生阅读检查结果的效率。 使用来自不同机构的mpMRI数据集，我们训练了ResNet、EfficientNet和DenseNet三种基于深度学习的分类器，并对其在分类八种不同MRI序列上的性能进行了比较。随后，确定表现最佳的分类器，在不同训练数据量设置下研究其分类能力，并在外部分布的数据集上对其进行评估。此外，我们还采用来自不同扫描仪的不同策略对模型进行训练，并测试了其性能。 实验结果显示，DenseNet-121模型在所有其他分类模型中取得了最高的F1分数和准确率，分别为0.966和0.972（p值<0.05）。当使用超过729项研究的训练数据进行训练时，该模型显示出大于0.95的精度，并且随着训练数据量的增长，其性能进一步提升。在外部分布的数据集DLDS和CPTAC-UCEC上，该模型分别获得了0.872和0.810的准确率。 这些结果表明，在内部和外部数据集中，DenseNet-121模型在分类八种身体MRI序列类型的任务中都达到了很高的准确性。
URL
https://arxiv.org/abs/2506.15182
