SwinUNETR
_Trained by Margerie Huet Dastarac ._
_Training date: November2023 ._
## 1. Task Description
Segmentation of the body on the CT scan on a datasheet 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
## 3. Dataset
+ Location: Head and neck, oropharynx
+ Training set size: 60
+ Data type: CT scan and body contours
+ Resolution in mm: 3x3x3
+ Preprocessing
## Performance
+ TBD