Code updates

README.md

@@ -12,92 +12,82 @@ pretty_name: 3D Brain Structure MRI PCA
 ---
 
 ## 🧠 Model Summary
-# brain2vec
+# brain2vec_PCA
 A linear PCA model for brain structure T1 MRIs. The models takes in a 3d MRI NIfTI file and compresses to 1200 latent dimensions before reconstructing the image.
 
 
 # Training data
-[Radiata brain-structure](https://huggingface.co/datasets/radiata-ai/brain-structure): 3066 scans from 2085 individuals in the 'train' split. Mean age = 45.1 +- 24.5, including 2847 scans from cognitively normal subjects and 219 scans from individuals with an Alzheimer's disease clinical diagnosis.
+[radiata-ai/brain-structure](https://huggingface.co/datasets/radiata-ai/brain-structure): 3066 scans from 2085 individuals in the 'train' split. Mean age = 45.1 +- 24.5, including 2847 scans from cognitively normal subjects and 219 scans from individuals with an Alzheimer's disease clinical diagnosis.
 
 # Example usage
 ```
-# get brain2vec model repository
-git clone https://huggingface.co/radiata-ai/brain2vec
-cd brain2vec
+# get brain2vec_PCA model repository
+git clone https://huggingface.co/radiata-ai/brain2vec_PCA
+cd brain2vec_PCA
 
 # set up virtual environemt
-python3 -m venv venv_brain2vec
-source venv_brain2vec/bin/activate
+python3 -m venv venv_brain2vec_PCA
+source venv_brain2vec_PCA/bin/activate
 
 # install Python libraries
 pip install -r requirements.txt
 
 # create the csv file inputs.csv listing the scan paths and other info
-# this script loads the radiata-ai/brain-structure dataset
+# this script loads the radiata-ai/brain-structure dataset from Hugging Face
 python create_csv.py
 
-mkdir ae_cache
-mkdir ae_output
+mkdir pca_cache
+mkdir pca_output
 
 # train the model
-nohup python brain2vec.py train \
-  --dataset_csv /home/ubuntu/brain2vec/inputs.csv \
-  --cache_dir   ./ae_cache \
-  --output_dir  ./ae_output \
-  --n_epochs    10 \
-> train_log.txt 2>&1 &
-
-# run model inference to create *_embeddings.npz files
-python brain2vec.py infererence \
-  --dataset_csv home/ubuntu/brain2vec/inputs.csv \
-  --aekl_ckpt /home/ubuntu/brain2vec/autoencoder_final.pth \
-  --output_dir /home/ubuntu/brain2vec
+nohup python train_brain2vec_PCA.py --inputs_csv inputs.csv --output_dir ./pca_output --pca_type standard --n_components 1200 > train_log.txt 2>&1 &
+
+# model inference
+    python inference_brain2vec_PCA.py \
+        --pca_model /path/to/pca_model.joblib \
+        --input_images /path/to/img1.nii.gz /path/to/img2.nii.gz \
+        --output_dir /path/to/out
+    
+# or if you have a CSV with image paths:
+    python inference_brain2vec_PCA.py \
+        --pca_model /path/to/pca_model.joblib \
+        --csv_input /path/to/images.csv \
+        --output_dir /path/to/out
 ```
 
 # Methods
-transform:
-(80, 96, 80)
-pixdim=2
-10 epochs
-    max_batch_size: int = 2,
-    batch_size: int = 16,
-    lr: float = 1e-4,
-
-# References
-Puglisi
-Pinaya
+Input scan image dimensions are 113x137x113, 1.5mm^3 resolution, aligned to MNI152 space (see [radiata-ai/brain-structure](https://huggingface.co/datasets/radiata-ai/brain-structure)).  
+
+The image transform crops to 80 x 96 x 80, 2mm^3 resolution, and scales image intensity to range [0,1].  
+
+PCA is performed using [sklearn.decomposition.PCA](https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html).  
+
 
 # Citation
 ```
-@dataset{Radiata-Brain-Structure,
+@misc{Radiata-Brain2Vec-PCA,
   author    = {Jesse Brown and Clayton Young},
-  title     = {Brain-Structure: Processed Structural MRI Brain Scans Across the Lifespan},
+  title     = {brain2vec_PCA: A Linear PCA Model for Brain Structure T1 MRIs},
   year      = {2025},
-  url          = {https://huggingface.co/datasets/radiata-ai/brain-structure},
+  url       = {https://huggingface.co/radiata-ai/brain2vec_PCA},
   note      = {Version 1.0},
-  publisher    = {Hugging Face}
+  publisher = {Hugging Face}
 }
 ```
 
 # License
-MIT License
-
-Copyright (c) 2025
-
-Permission is hereby granted, free of charge, to any person obtaining a copy
-of this software and associated documentation files (the "Software"), to deal
-in the Software without restriction, including without limitation the rights
-to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
-copies of the Software, and to permit persons to whom the Software is
-furnished to do so, subject to the following conditions:
-
-The above copyright notice and this permission notice shall be included in all
-copies or substantial portions of the Software.
-
-THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
-IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
-FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
-AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
-LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
-OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
-SOFTWARE.
+### Apache License 2.0
+
+Copyright 2025 Jesse Brown
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at:
+
+[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.