Comments (3)
Hi @hanoonaR
Thanks for the comments. The validation accuracy reported in the repo denotes for a single fold and by splitting the publicly available training data. For the leaderboard however, following the approach by previous SOTA for both models, we have used an ensemble of 20 models and extra private training data which increases the number of samples to 80 volumes. This is described in section 4.3 of the UNETR paper.
I hope this addresses the concerns.
Best,
from research-contributions.
Similar issue in another dataset (similar to BTCV), these two models are even worse than 3dunet.
from research-contributions.
Hi @lbf4616
Thanks for the comment. Based on our experience for both models, each task requires careful tuning to achieve optimal performance. For instance, we note that choice of optimizer, which is AdamW in our case, the learning rate scheduler and number of epochs could play an important role to achieve the best performance.
Overall, in most MSD tasks and BTCV, our model consistently outperformed unet or nnunet, if carefully tuned.
Best,
from research-contributions.
Related Issues (20)
- Swin-UNETR pretraining model weights do not load into the model
- Validation
- One problem came when I was using SwinUNETR to train my own data HOT 5
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- SwinUNETR-V2
- About Test HOT 4
- about evaluation HOT 1
- About SwimUNETR Model Structure
- Unnecessary conv3 Layer Weights in DAE with MONAI v0.8.0 Leading to Compatibility Issues with v0.9.0+
- FileNotFoundError in swinunetr
- Swin-UNETR pretraining validation criterion
- AttributeError: 'NoneType' object has no attribute 'items' in Auto3dSeg
- Not reproducible results
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- Brats2020 HOT 3
- Evaluation
- difference brats2021 train test
- Unexpected key(s) in state_ dict: "epoch", "best_acc", "state_dict". HOT 3
- N foreground 0, N background 52003401,unable to generate class balanced samples. NaN or Inf found in input tensor.
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