Comments (6)
추가로 weighted_label_dice_coefficient_loss를 주었음
from 3d_coordconv_segmentation.
CT가 MR보다 더 심장이 많이 보여져서 그런지 학습속도는 확실히 더 빠르다.
MR
Epoch 19/500 - 14s
- loss: -9.9746e-03 - dice_coefficient: ### 0.0163
- label_0_DSC: 0.0161 - label_1_DSC: 0.0312 - label_2_DSC: 0.0325 - label_3_DSC: 6.1031e-05 - label_4_DSC: 6.1031e-05 - label_5_DSC: 6.1031e-05 - label_6_DSC: 6.1031e-05 - label_7_DSC: 6.1031e-05
- val_loss: -1.1937e-02 - val_dice_coefficient: 0.0169
- val_label_0_DSC: 0.0164 - val_label_1_DSC: 0.0498 - val_label_2_DSC: 0.0293 - val_label_3_DSC: 6.1031e-05 - val_label_4_DSC: 6.1031e-05 - val_label_5_DSC: 6.1031e-05 - val_label_6_DSC: 6.1031e-05 - val_label_7_DSC: 6.1031e-05
CT
Epoch 19/30 - 14s
- loss: -1.1292e-02 - dice_coefficient: 0.0167
- label_0_DSC: 0.0162 - label_1_DSC: 0.0402 - label_2_DSC: 0.0340 - label_3_DSC: 6.1031e-05 - label_4_DSC: 6.1031e-05 - label_5_DSC: 6.1031e-05 - label_6_DSC: 6.1031e-05 - label_7_DSC: 6.1031e-05
- val_loss: -1.3186e-02 - val_dice_coefficient: 0.0175
- val_label_0_DSC: 0.0165 - val_label_1_DSC: 0.0529 - val_label_2_DSC: 0.0361 - val_label_3_DSC: 6.1031e-05 - val_label_4_DSC: 6.1031e-05 - val_label_5_DSC: 6.1031e-05 - val_label_6_DSC: 6.1031e-05 - val_label_7_DSC: 6.1031e-05
from 3d_coordconv_segmentation.
mask label 값이 전처리에서 잘못돼서 학습이 안됐음
from 3d_coordconv_segmentation.
전처리를 제대로 진행하고 해도 학습이 잘 되지 않는다. 전체 class에 대해서 학습이 되지만 DSC가 10%를 넘는 class가 없다.
그래서 2D로 이미지를 바꿔서 진행하고 있다.
from 3d_coordconv_segmentation.
2D image가 성능이 잘 나오지 않아서 binary로 background와 아닌것을 기준으로 학습했더니 엄청 잘나왔다.
학습 속도 면에서도 2D U net은 epoch당 160초, 3D U net은 epoch당 30초 정도의 시간이 걸렸다.
한가지 3D U net에서 참고해햐할 부분은 10epoch 정도에서는 크게 학습이 잘 되지 않다가 갑자기 70%로 학습이 확 올랐다.
from 3d_coordconv_segmentation.
Fine tuning을 해봐야할것같다.
from 3d_coordconv_segmentation.
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from 3d_coordconv_segmentation.