Update README.md

README.md

@@ -1,52 +1,55 @@
-<h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> HDUNet </h1> 
+<h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> SwinUNETR </h1> 
 
 _Trained by Margerie Huet Dastarac ._ <br> 
-_Training date: 05/05/2023 ._ 
+_Training date: November 2023 ._ 
 
 ## 1. Task Description 
-Dose prediction 
+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 
 
 ![image/png]( https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/7X1GxxIT2LlpPBdR_tCzt.png )
 
-_Figure 1: HDUNet architecture_ 
+_Figure 1: SwinUNETR architecture_ 
+
 ### 2.2. Input 
 <ul>
 <li> CT</li>
-<li> Target volumes</li>
-<li> Organ at risks masks</li>
 </ul>
 
 ### 2.3. Output 
 <ul>
-<li> DOSE</li>
+<li> BODY</li>
 </ul>
 
 ### 2.4 Training details 
 <ul> 
-<li> Number of epoch: 400 </li> 
-<li> Loss function: MSE loss </li> 
-<li> Optimizer: AdamW </li> 
-<li> Learning Rate: 0.0001 </li> 
+<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> RandCrop</li>
 <li> RandSpatialCropd</li>
-<li> NormalizedIntensityd</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: Oropharynx </li> 
-<li> Training set size: 57 </li> 
+<li> Location: Head and neck, oropharynx </li> 
+<li> Training set size: 60 </li> 
 <li> Resolution in mm: 3x3x3 </li> 
 </ul> 
 
 ## Performance 
-+ TBD 
++ TBD