基于代价敏感Faster R-CNN的脑CT影像出血诊断方法

Diagnosis of Intracranial Hemorrhage in Brain CT Images Based on Cost-Sensitive Faster R-CNN

脑出血是一种在患者颅内突然发生的重急症,常伴随有强烈的症状和较高致死率,基于脑CT影像对脑出血进行自动化快速诊断具有重要意义。其中,临床上有效应用的实现不仅要求诊断结果的准确性、诊断速度和结果解释能力,尤其要重视出血漏检情形。因此本文提出代价敏感的Faster R‑CNN模型,通过自动调节模型中锚的训练样本比例以及在损失函数中引入衡量阳性样本重要性的超参数等方式,更多地关注阳性样本和漏检情形提升检测效果,最后通过定位的具体目标区域来诊断脑内出血情况。经多次实验选择性能最优的网络结构和合适的超参数,利用多项指标度量最终模型的检测和诊断效果。实验结果表明,代价敏感的FasterR‑CNN方法能够从减少漏检的角度上更好地识别出血区域,进而提高不平衡代价下的脑出血诊断效果。

An intracranial hemorrhage(ICH)is a kind of severe emergency that occurs suddenly in patients’brain with strong symptoms and high mortality.So it is of great significance to diagnose ICH automatically and quickly based on brain CT images.However,effective clinical application requires not only the accuracy,speed and interpretation ability of models,but also especially the emphasis given to the missed detection of bleeding.Therefore,cost-sensitive Faster R-CNN is proposed in this paper to diagnose ICH,through an automatic adjustment mechanism for the proportion of training samples and a hyperparameter introduced to loss function to measure the importance of positive samples.It can pay more attention to the missed detection situations to improve the detection effect,and diagnose ICH by located target region.A network structure with optimal performance and appropriate parameter is selected for good effect of detection and diagnosis through experiments.And then,results are measured by several indexes.It is shown that the cost-sensitive Faster R-CNN model can detect bleeding well by focusing on missed checks,so as to improve the diagnosis effect under the unbalanced cost.