Update README.md

README.md

@@ -1,40 +1,48 @@
-<h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> SwinUNETR </h1>
+<h1 style="border-bottom: 2px solid black; font-size: 100px;" align="center"> HDUNet </h1> 
 
-_Trained by Margerie Huet Dastarac ._ <br>
-_Training date: November2023 ._
+_Trained by Margerie Huet Dastarac ._ <br> 
+_Training date: 05/05/2023 ._ 
 
-## 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.
+## 1. Task Description 
+Dose prediction 
 
-## 2. Model
-### 2.1. Architecture
+## 2. Model 
+### 2.1. Architecture 
 
 ![image/png]( https://cdn-uploads.huggingface.co/production/uploads/65c9dbefd6cbf9dfed67367e/7X1GxxIT2LlpPBdR_tCzt.png )
 
-_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
+_Figure 1: HDUNet 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>
+</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> 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>
+</ul>
+</li> 
+</ul> 
+## 3. Dataset 
+<ul> 
+<li> Location: Oropharynx </li> 
+<li> Training set size: 57 </li> 
+<li> Resolution in mm: 3x3x3 </li> 
+</ul> 
+## Performance 
++ TBD