Comments (5)
Hi,
I am very appreciate for your reply, and I have figured out this.
Thanks again for your great work and your kindful reply!!!
Merry Christmas in advance:)
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Thanks so much for the reply. May I know where the additional 50 dataset you get to make the training to 80 data?
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Thanks so much for the reply. May I know where the additional 50 dataset you get to make the training to 80 data?
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Hi @Puzzled-Hui
Thanks for the comments and questions. There are some differences that cause the discrepancies in the numbers you mentioned. Firstly, our goal in the tutorial was to relax the memory constrains and making it more accessible for users. For this purpose, the UNETR model is trained on a different resolution which is 1.5* 1.5 * 2 as opposed to 1* 1* 1. Secondly, we only trained on 1 fold without any extensive data augmentation. For the paper, we utilized a five-fold cross validation scheme and used ensemble of 10 different models, from two different splits, for test server submission. Also noting that, the 0.79 performance comes from training on a single fold as outlined in the tutorial and research contribution repository ( link to json file)
In addition, as you are aware, BTCV server has 2 different submission tracks, namely standard and free competition. For the free competition, it is customary to use extra training data as done by previous works that are listed on the challenge leaderboard. Considering this, we also increased the dataset size to 80 to be comparable with these approaches and again utilized five fold cross validation for test server submission.
I hope these explanations are useful.
Thanks
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Hi @Puzzled-Hui
Thanks for the comments and compliments.
Happy Holidays :)
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Related Issues (20)
- Auto3dseg autorunner very slow/unclear output
- Questions regarding accelerating training speed
- RuntimeError: CUDA out of memory HOT 2
- How should I reproduce the training process of Swin UNETR on BTCV so that it can achieve the mean dice score recorded in the paper? HOT 1
- While fine tuning the Swin UNETR, Training loss is not decreasing and training gets crashed after 10 epohs HOT 7
- normalization into [0, 1] using window width/level of 1500/-650 HOT 3
- Training error on BRATS21 HOT 8
- Do the test data work under the test.py file? HOT 1
- RuntimeError: applying transform <monai.transforms.io.dictionary.LoadImaged object at 0x7f0f84fb8a90> HOT 6
- UNetFormer: A Unified Vision Transformer Model and Pre-Training Framework for 3D Medical Image Segmentation HOT 2
- Training model cannot load properly HOT 2
- Add version control for the auto3dseg
- AttributeError: module 'torch._C' has no attribute '_cuda_setDevice' HOT 1
- Converting SwinUNETR to ONNX or Tensorflow
- RuntimeError: applying the transform < monai. Transforms. Spatial. Dictionary. Orientationd object at 0 x7f791cb8bfd0 >
- Problem with the number of output channels in brats data sets HOT 2
- Unable to run prostate-mri-lesion-seg on Colab HOT 6
- Unable to run the prostate-mri-lesion-seg tutorial notebook using jupyter HOT 6
- Why is there a significant difference in the maximum number of epochs required for different segmentation tasks? HOT 4
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