论文标题
一种基于中心的新型深层对比度度量学习方法，用于检测小儿大脑MRI中多糖蛋白
A Novel Center-based Deep Contrastive Metric Learning Method for the Detection of Polymicrogyria in Pediatric Brain MRI
论文作者
论文摘要
多糖蛋白酶（PMG）是一种主要在儿童中看到的皮质组织疾病，可以与癫痫发作，发育延迟和运动无力有关。 PMG通常在磁共振成像（MRI）上被诊断出，但是即使对于经验丰富的放射学家，某些情况也可能具有挑战性。在这项研究中，我们创建了一个带有PMG的开放小儿MRI数据集（PPMR），并从加拿大渥太华的安大略省儿童医院（Cheo）的儿童医院进行了对照。 PMG MRI和对照MRI之间的差异是微妙的，疾病特征的真实分布尚不清楚。这使得难以自动检测MRI中潜在PMG的病例。我们提出了一种基于新型的基于中心的深层对比度学习损失函数（CDCM）的异常检测方法，该方法可以自动检测潜在PMG病例。此外，根据我们提出的损失函数，我们自定义了深度学习模型结构，该结构集成了扩张的卷积，挤压和激发块和PPMR数据集的功能融合。尽管使用了一个小且不平衡的数据集，但我们的方法以55.04％的精度达到92.01％的召回率。这将促进放射科医生选择潜在PMG MRI的计算机辅助工具。据我们所知，这项研究是第一个应用机器学习技术仅从MRI识别PMG的研究。
Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) with PMG and controls from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG MRIs and control MRIs are subtle and the true distribution of the features of the disease is unknown. This makes automatic detection of cases of potential PMG in MRI difficult. We propose an anomaly detection method based on a novel center-based deep contrastive metric learning loss function (cDCM) which enables the automatic detection of cases of potential PMG. Additionally, based on our proposed loss function, we customize a deep learning model structure that integrates dilated convolution, squeeze-and-excitation blocks and feature fusion for our PPMR dataset. Despite working with a small and imbalanced dataset our method achieves 92.01% recall at 55.04% precision. This will facilitate a computer aided tool for radiologists to select potential PMG MRIs. To the best of our knowledge, this research is the first to apply machine learning techniques to identify PMG from MRI only.
