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AngeLouCN / CaraNet

Context Axial Reverse Attention Network for Small Medical Objects Segmentation
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About the main.m #5

Closed 15104356116 closed 2 years ago

15104356116 commented 2 years ago

Hi, sorry to bother. I'm wondering why I got all the Dice with NaN. I'v check the pathes, but still got NaN in each Dice. cell. The _result.txt is like "(Dataset:CVC-ClinicDB; Model:PraNet) meanDic:NaN;meanIoU:NaN;wFm:NaN;Sm:NaN;meanEm:NaN;MAE:NaN;maxEm:NaN;maxDice:NaN;maxIoU:NaN;meanSen:NaN;maxSen:NaN;meanSpe:NaN;maxSpe:NaN. "

AngeLouCN commented 2 years ago

Hi, I think you should change the model name as 'CaraNet'. As shown in the Test.py, the result are saved in the './results/CaraNet/'

15104356116 commented 2 years ago

OMG! Thanks a lot for your reply ！

15104356116 commented 2 years ago

By the way, if my data set is MRI , and I change the Traning data to MRI for training, I wonder will the module still work on that ?

AngeLouCN commented 2 years ago

I think you need to convert 3-D volume to 2-D slicer and then feed them to network.

15104356116 commented 2 years ago

Thank you! 🥰🥰🥰