My model is a SegNet with cross-entrophy loss. It is for liver and liver vessel medical segmentation. And my problem is, when I use 725 cases for input, the accuracy increase normally. The result of liver is good, but the result of vessel still need to be improve. But when I increase my input data to 34,868 cases, the accuracy doesn't increase at the beginning, and then jump up to about 95% accuracy. Even the accuracy is about 95%, the result of my segmentation is very poor. The result got no liver nor liver vessel, but full of background. The training process with input 725 cases The training process with input 34,868 cases
I am wondering if my model is too complicated, or I should modified the loss function. I have change my loss function to focal loss and dice+focal loss, but they doesn't solve the problem or improve the result.
