region:us
<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 
