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<h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> SwinUNETR </h1>
_Trained by Margerie Huet Dastarac ._ <br>
_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
<img width="100%" src="https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/o59In69BqrxTEoOdSHZLD.png" alt="alternatetext">
_Figure 1: SwinUNETR architecture_
### 2.2. Input
<ul>
<li> CT</li>
</ul>
### 2.3. Output
<ul>
<li> BODY</li>
</ul>
### 2.4 Training details
<ul>
<li> Number of epoch: 300 </li>
<li> Loss function: Dice loss </li>
<li> Optimizer: Adam </li>
<li> Learning Rate: 3e-4 </li>
<li> Dropout: No </li>
<li> Patch size in voxels: (128,128,128) </li>
<li> Data augmentation used:
<ul>
<li> RandSpatialCropd</li>
<li> RandFlipd axis:0</li>
<li> RandFlipd axis:1</li>
<li> RandFlipd axis:2</li>
<li> NormalizeIntensityd</li>
<li> RandScaleIntensityd factors:0.1 prob:1.0</li>
<li> RandShiftIntensityd, offsets:0.1, prob:1.0</li>
</ul>
</li>
</ul>
## 3. Dataset
<ul>
<li> Location: Head and neck, oropharynx </li>
<li> Training set size: 60 </li>
<li> Resolution in mm: 3x3x3 </li>
</ul>
## Performance
+ TBD
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