用混合模型提升胎盘植入谱系MRI自动诊断准确率。
Placenta Accreta Spectrum Detection Using an MRI-based Hybrid CNN-Transformer Model
- 结合3D DenseNet提取局部特征，ViT建模全局空间关系。
- 在1133例数据上测试，平均准确率达84.3%。
- 适合临床辅助诊断，提升放射科医生判读一致性。
胎盘植入谱系（PAS）是一种严重的产科疾病，其诊断常因放射科医生解读差异而具有挑战性。为克服此问题，本研究提出一种基于3D MRI图像的混合深度学习模型，用于全自动检测PAS。该模型融合3D DenseNet121以捕获局部特征，并引入3D Vision Transformer（ViT）以建模全局空间上下文。模型在包含1,133例体积MRI数据的回顾性数据集上进行开发与评估，同时对比了多种3D深度学习架构。在独立测试集上，DenseNet121-ViT模型表现最优，五次运行平均准确率为84.3%。结果表明，混合CNN-Transformer模型具备强大的计算机辅助诊断潜力，可有效辅助放射科医生，提高诊断一致性、准确性和及时性。
原文摘要 · Abstract (English)
Placenta Accreta Spectrum (PAS) is a serious obstetric condition that can be challenging to diagnose with Magnetic Resonance Imaging (MRI) due to variability in radiologists' interpretations. To overcome this challenge, a hybrid 3D deep learning model for automated PAS detection from volumetric MRI scans is proposed in this study. The model integrates a 3D DenseNet121 to capture local features and a 3D Vision Transformer (ViT) to model global spatial context. It was developed and evaluated on a retrospective dataset of 1,133 MRI volumes. Multiple 3D deep learning architectures were also evaluated for comparison. On an independent test set, the DenseNet121-ViT model achieved the highest performance with a five-run average accuracy of 84.3%. These results highlight the strength of hybrid CNN-Transformer models as a computer-aided diagnosis tool. The model's performance demonstrates a clear potential to assist radiologists by providing a robust decision support to improve diagnostic consistency across interpretations, and ultimately enhance the accuracy and timeliness of PAS diagnosis.
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