Update README.md

README.md

@@ -70,6 +70,74 @@ MICCAI 2025 <br/>
 *Johns Hopkins University* <br/>
 <a href='https://www.zongweiz.com/dataset'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://www.cs.jhu.edu/~zongwei/publication/li2025pants.pdf'><img src='https://img.shields.io/badge/Paper-PDF-purple'></a>
 
+
+
+
+# Inference
+
+**0- Download and installation.**
+
+
+<details>
+<summary style="margin-left: 25px;">[Optional] Install Anaconda on Linux</summary>
+<div style="margin-left: 25px;">
+    
+```bash
+wget https://repo.anaconda.com/archive/Anaconda3-2024.06-1-Linux-x86_64.sh
+bash Anaconda3-2024.06-1-Linux-x86_64.sh -b -p ./anaconda3
+./anaconda3/bin/conda init
+source ~/.bashrc
+```
+</div>
+</details>
+
+```
+git clone https://github.com/MrGiovanni/R-Super
+cd R-Super/rsuper_train
+conda create -n rsuper python=3.10
+conda activate rsuper
+pip install -r requirements.txt
+pip install -U "huggingface_hub[cli]"
+hf download AbdomenAtlas/MedFormerPanTS --local-dir ./MedFormerPanTS
+```
+
+**1- Pre-processing.** Prepare your dataset in the format below. You can use symlinks instead of copying your data.
+<details>
+<summary style="margin-left: 25px;">Dataset format.</summary>
+<div style="margin-left: 25px;">
+
+```
+/path/to/dataset/
+├── BDMAP_0000001
+|    └── ct.nii.gz
+├── BDMAP_0000002
+|    └── ct.nii.gz
+...
+```
+</div>
+</details>
+
+**2- Inference.** The code below will inference, generating binary segmentation masks. To save probabilities, add the argument --save_probabilities or --save_probabilities_lesions (which saves only probabilities for lesions, not for organs). The optional argument --organ_mask_on_lesion will use organ segmentations (produced by the R-Super model itself, not ground-truth) to remove tumor predictions outside its organ. 
+
+```bash
+python predict_abdomenatlas.py --load MedFormerPanTS/pants_pancreas_release/fold_0_latest.pth --img_path /path/to/test/dataset/ --class_list MedFormerPanTS/labels_pants.yaml --save_path /path/to/inference/output/ --organ_mask_on_lesion
+```
+<details>
+<summary style="margin-left: 25px;"> Argument Details </summary>
+<div style="margin-left: 25px;">
+  
+- load: path to the model checkpoint (fold_0_latest.pth)
+- img_path: path to dataset
+- class_list: a yaml file with the class names of your model
+- save_path: path to output, where masks will be saved
+- ids: this is an optional argument. By default, the code will predict on all cases in --img_path. If you pass ids, the code will only test with the CT scans indicated in ids. You can use this to separate a test set: --ids /path/to/test/set/ids.csv. The csv file must have a 'BDMAP ID' column with the ids of the test cases.
+
+</details>
+
+For more details, see https://github.com/MrGiovanni/R-Super/tree/main/rsuper_train#test
+
+
+
 # Citations
 If you use this data, please cite the 2 papers below: