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SwinUNETR
Trained by Margerie Huet Dastarac .
Training date: November 2023 .
1. Task Description
Segmentation of the body on the CT scan on a dataset of 60 oropharyngeal patients. This model can be used to clean CT scans by setting voxels value outside of the body contour to air, a typical preprocessing step for other networks.
2. Model
2.1. Architecture
Figure 1: SwinUNETR architecture
2.2. Input
- CT
2.3. Output
- BODY
2.4 Training details
- Number of epoch: 300
- Loss function: Dice loss
- Optimizer: Adam
- Learning Rate: 3e-4
- Dropout: No
- Patch size in voxels: (128,128,128)
- Data augmentation used:
- RandSpatialCropd
- RandFlipd axis:0
- RandFlipd axis:1
- RandFlipd axis:2
- NormalizeIntensityd
- RandScaleIntensityd factors:0.1 prob:1.0
- RandShiftIntensityd, offsets:0.1, prob:1.0
3. Dataset
- Location: Head and neck, oropharynx
- Training set size: 60
- Resolution in mm: 3x3x3
Performance
- TBD
Inference Providers
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