重加权在多类别不平衡医学图像检测中的应用

Application of Re-Weight Method in Multiple Class-Imbalance Medical Images Detection

在医学图像检测中,由于数据集经常存在每类样本数目不均衡的情况,使数据集样本出现长尾分布的问题,严重影检测模型的性能。针对网络在训练多类别不均衡数据集中训练时出现的过拟合现象,采用重加权的方式改进原有损失函数,并用CLAHE算法对X光图像进行预处理,以突出图像的内部细节,选用ResNext50网络作为特征提取网络。以covid-chestxray数据集作为实验用数据集,通过实验评估了模型的准确度、精确率、召回率和F1值,证实了该方法的有效性。

In medical image detection, the dataset often has a situation od class-imbalanced, which causes the long-tail distribution problem in the dataset samples and the performance of the detection model becomes seriously. This paper aims to solve the over-fitting problem of the network when training with multiple class-imbalance dataset, improving the original loss function by re-weight method, and preprocessing the X-ray image by CLAHE algorithm to highlight the internal details of the image. Finally, ResNext50 is selected as the feature extraction network. By using covid-chestxray dataset as the dataset, this paper evaluates the Accuracy, Precision, Recall rate and F1 value of the model. The effectiveness of this method is verified in experiments.