论文标题
使用基于斑块的合奏和心脏MRI中的转移学习与运动有关的人工伪像分类
Motion-related Artefact Classification Using Patch-based Ensemble and Transfer Learning in Cardiac MRI
论文作者
论文摘要
心脏磁共振成像（MRI）在心脏功能分析中起重要作用。但是，由于呼吸困难，尤其是对于急性症状患者而言，收购通常伴随着运动伪像。因此，必须评估心脏MRI的质量以进行进一步分析。耗时的基于手动的分类不利于构建端到端的计算机辅助诊断系统。为了克服这个问题，在这项工作中提出了使用集合和转移学习的自动心脏MRI质量估计框架。初始化了多个预训练的模型，并在训练数据中采样的二维图像贴片上进行了微调。在模型推理过程中，这些模型的决策是汇总的，以做出最终预测。该框架已在CMRXMotion Grand Challenge（MICCAI 2022）数据集上进行了评估，该数据集很小，多级且不平衡。它在训练集（5倍交叉验证）和验证集的分类精度分别达到了78.8％和70.0％。最终训练的模型还对CMRXMOTION组织者的独立测试进行了评估，CMRXMOTION组织者的分类准确性为72.5％，Cohen的Kappa为0.6309（在这个大挑战中排名最高）。我们的代码可在github上找到：https：//github.com/ruizhe-l/cmrxmotion。
Cardiac Magnetic Resonance Imaging (MRI) plays an important role in the analysis of cardiac function. However, the acquisition is often accompanied by motion artefacts because of the difficulty of breath-hold, especially for acute symptoms patients. Therefore, it is essential to assess the quality of cardiac MRI for further analysis. Time-consuming manual-based classification is not conducive to the construction of an end-to-end computer aided diagnostic system. To overcome this problem, an automatic cardiac MRI quality estimation framework using ensemble and transfer learning is proposed in this work. Multiple pre-trained models were initialised and fine-tuned on 2-dimensional image patches sampled from the training data. In the model inference process, decisions from these models are aggregated to make a final prediction. The framework has been evaluated on CMRxMotion grand challenge (MICCAI 2022) dataset which is small, multi-class, and imbalanced. It achieved a classification accuracy of 78.8% and 70.0% on the training set (5-fold cross-validation) and a validation set, respectively. The final trained model was also evaluated on an independent test set by the CMRxMotion organisers, which achieved the classification accuracy of 72.5% and Cohen's Kappa of 0.6309 (ranked top 1 in this grand challenge). Our code is available on Github: https://github.com/ruizhe-l/CMRxMotion.
