问题描述
我使用Unet架构进行肺分割，它向我展示了更好的训练和Val损失，但是当我调用预测功能并给一个训练集图像作为输入时，给了我空白图像作为输出.我了解为什么这样做当它显示出良好的验证准确性时.我正在使用keras
I m using Unet architecture for lung segmentation it show me better training and Val loss but when I call predict function and give one image of training set as input.its gives me blank image as output .I am understanding why is doing so when it show good validation accuracy.I'm using keras
推荐答案
准确度不是用于细分的良好指标，尤其是对于医疗案例而言.您的数据集很可能是不平衡的，这就是为什么您获得高精度的原因(大多数地面真相掩模像素为"0").您应该使用Dice，precision/recall/F1等指标来跟踪结果
Accuracy is not a good metrics for segmentation, especially for medical cases. Your dataset is most likely imbalanced which is why you are getting high accuracy (most of the ground truth mask pixels are "0"). You should use metrics like Dice, precision/recall/F1 to track results
这篇关于Unet分割模型可预测空白图像吗?的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！