Despite the successes of deep learning techniques at detecting objects in medical images, false positive detections occur which may hinder an accurate diagnosis. We propose a technique to reduce false positive detections made by a neural network using an SVM classifier trained with features derived from the uncertainty map of the neural network prediction. We demonstrate the effectiveness of this method for the detection of liver lesions on a dataset of abdominal MR images. We find that the use of a dropout rate of 0.5 produces the least number of false positives in the neural network predictions and the trained classifier filters out approximately 90% of these false positives detections in the test-set.
翻译:尽管在探测医学图像中的物体方面取得了深层次学习技术的成功,但假阳性检测仍会发生,这可能会妨碍准确诊断。我们建议采用一种技术来减少神经网络使用SVM分类器进行的假阳性检测,该分类器经过培训,其特征来自神经网络预测的不确定地图。我们证明这种方法在检测腹部MR图像数据集中的肝脏损伤方面的有效性。我们发现,在神经网络预测和经过培训的分类器过滤器中,使用0.5的辍学率产生了最少的假阳性,测试集中大约90%的假阳性检测结果。