About the performance on ISIC2017 dataset
wangyxxjtu opened this issue · 6 comments
Hello,
Great Work!
I have some questions about the results on ISIC2017(skin cancer segmentation dataset). I trained the model using your code, and got satisfied results on DRIVE (eye ) and Lung segmentation datasets, but I can't get normal results on ISIC 2017, the performance is very poor, the dc is only 0.34. So, Do you have any idea about the problem?
Thanks.
Did you check the intermediate training images and see if the segmentation is detecting the location of Gt correctly?
Thank you for your reply.
Following your suggestion, I randomly visualized several images and the corresponding masks during tranining, all these samples are exactly matched.
Ok.
And all the Unets are showing the same results?
Did you try with Nested-Unet?
Thank you.
I found the problem, I made some changes on data transforms in training, learning to the inconsistent between the training data and the testing data. Unfortunately, after I fixed the data transform problem, the performance is still not reasonable, for unet on ISIC 2017, the DC is only 0.69 (0.86 reported in r2u-net paper), and the nestedu- net is only 0.73, which are much worse.
Try to increase the depth of the model or decrease it.
Maybe that can provide a better dice score.
Ok, thank you.