Breast_Cancer_Detection

Experimenting with public breast cancer mammography datasets (InBreast, MIAS, CBIS-DDSM). Deep learning project focused on mammography screening and expert aid.

Features

• Deep learning model for mass detection (using Detectron2 and Ultralytics)
  • API
  • Web GUI
  • Train and evaluate
• Explainable AI
  • ResNet
  • API
• Chatbot LLM API
  • Combining previous model prediction with expert opinion
• Radiomics

Prerequisites

• Nvidia CUDA drivers
  • Install a PyTorch compatible version of CUDA from:
    • Your Linux repository

    apt install nvidia-cuda-toolkit
    

    • NVIDIA website for Windows and Linux
      • Link to download page
• Pytorch with CUDA support
  • Visit PyTorch website for more information

These two must be installed manually or else will break installation of other requirements later on.

Datasets

Supported datasets:

• InBreast
• CBIS-DDSM (Curated Breast Imaging Subset of DDSM)
• MIAS (Mammography Image Analysis Society)

Supported models:

• Generally supported models
  • Faster R-CNN (Detectron)
  • YOLO
  • Any model that supports YOLO / COCO style dataset
• Customized UaNet for 2D mammography images

Download

Google Colab

• Use download_datasets_colab.ipynb jupyter notebook in Google Colab to download all datasets.
• You will need to upload your 'kaggle.json' when the notebook gives you an upload dialog.
• After logging in to kaggle, you can get your kaggle json in API section of https://www.kaggle.com/settings.
• The notebook will clone this repository and download all datasets.

Manual

Dataset links:

• https://www.kaggle.com/datasets/ramanathansp20/inbreast-dataset
• https://www.kaggle.com/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset
• https://www.kaggle.com/datasets/kmader/mias-mammography

Download the above datasets and after cloning this repository, create the following directories:

• breast_cancer_detection/
  • datasets/
    • all-mias/
      • mdb001.pgm
      • ...
    • CBIS-DDSM/
      • csv/
      • jpeg/
    • INbreast Release 1.0/
      • AllDICOMs/
      • ...

Copy datasets to directories accordingly.

Visualizer

After converting the datasets to COCO / YOLO style in the next section (Usage), you may visualize the standardized dataset using the following methods.

COCO Style dataset

python visualizer.py -m coco -d train/images -l train.json 

YOLO Style dataset

python visualizer.py -m yolo -d train/images -l train/labels 

Usage

1. Clone this repository

git clone https://github.com/monajemi-arman/breast_cancer_detection

2. Install prerequisites

cd breast_cancer_detection
pip install --no-build-isolation -r requirements.txt

2. Download the following datasets
https://www.kaggle.com/datasets/ramanathansp20/inbreast-dataset
https://wiki.cancerimagingarchive.net/pages/viewpage.action?pageId=22516629
https://www.kaggle.com/datasets/kmader/mias-mammography

3. Move dataset files
First create 'datasets' directory:

mkdir datasets/

Then, extract and move the files to this directory so as to have the following inside datasets/:

• INbreast Release 1.0/
• CBIS-DDSM/
• all-mias/

4. Convert datasets to YOLO (and COCO) format

python convert_dataset.py

After completion, images/, labels/, dataset.yaml, annotations.json would be present in the working directory.

5. (optional) Apply additional filters to images
If necessary, you may apply these filters to images using our script: _canny, clahe, gamma, histogram, unsharp
You may enter one of the above filters in command line (-f).

python filters.py -i PATH_TO_IMAGE_DIRECTORY -o OUTPUT_IMAGE_DIRECTORY -f FILTER_NAME

---

Detectron (Faster R-CNN)

Train

The purpose of detectron.py is to train and evaluate a Faster R-CNN model and predict using detectron2 platform.

python detectron.py -c train

Predict

• Visualize model prediction
• Show ground truth and labels
• Filter predictions by confidence score

# After training is complete
python detectron.py -c predict -w output/model_final.pth -i <image path>
# -w: path to model weights

Web Application

Usage

1. Run train step as explained above
2. Copy 'detectron.cfg.pkl' and the last model checkpoint to webapp/ directory.
   * Last model checkpoint file name is written in output/last_checkpoint
3. Run the following:

cd webapp/
python web.py

4. Then visit http://127.0.0.1:33517

5. (optional) Use API
   If you wish, API is also available, example:

# Run server
cd webapp/
python web.py

# Get predictions
curl -X POST \
  -F "file=@input.jpg" \
  http://localhost:33517/api/v1/predict \
  | jq -r '.data.inferred_image' | base64 --decode > prediction.jpg
  
# You may also pass several files for batch prediction
curl -X POST \
  -F "file=@sample1.jpg" \
  -F "file=@sample2.jpg" \
  http://localhost:33517/api/v1/predict  # Returns prediction array

Evaluate

Evaluation using COCOEvaluator

• Calculate mAP
• Uses test dataset by default

python detectron.py -c evaluate -w output/model_final.pth

Save predictions in COCO style JSON (optional)

• Suitable for later offline metrics calculation
• All predictions of the test dataset will be written to predicions.json
• Follows COCO format

python detectron.py -c evaluate_test_to_coco -w output/model_final.pth

---

Explainable AI

First you must train classification model on the data.
The datasets contain data suitable for object detection. Therefore, you must first convert into classification dataset:

• Convert dataset for classification

python coco_to_classification.py train.json train_class.json

• Train classification model

python classification_model.py -a train_class.json -d train/images --save_dir classification_output -c train

• Generate XAI

python classification_model.py --save_dir classification_output -c predict -i train/images/cb_1.jpg

• Run & Test API

python classification_model.py --save_dir classification_output -c api

# Save sample to heatmap.jpg
curl -X POST -F "file=@test/images/20586986.jpg" http://localhost:33519/predict | jq -r '.activation_map' | base64 -d >~/heatmap.jpg

---

LLM API

• Setup config
  Inside llm/ directory, create 'config.json' based on 'config.json.default' template.

• Run & Test LLM API

python llm/llm_api_server.py

curl -X POST http://localhost:33518/generate-response \                  
-H "Content-Type: application/json" \
-d '{
  "prompt": "What is BI-RADS 4?", "predictions": "Some preds"
}'

---

YOLO

Training

• Install Ultralytics

pip install ultralytics

• Train your desired YOLO model

yolo train data=dataset.yaml model=yolov8n

Prediction

Example of prediction using YOLO ultralytics framework:

yolo predict model=runs/detect/train/weights/best.pt source=images/cb_1.jpg conf=0.1 

---

UaNet (Deprecated)

Training

• Clone UaNet repository (patched)

# Make sure you cd to breast_cancer_detection first
# cd breast_cancer_detection
git clone https://github.com/monajemi-arman/UaNet_2D

• Prepare dataset

# Convert datasets to images/ masks/
python convert_dataset.py -m mask
# Convert to 3D NRRD files
python to_3d_nrrd.py

• Move dataset to model directory

# While in breast_cancer_detection directory
mv UaNet-dataset/* UaNet_2D/data/preprocessed/
# Remove old default configs of UaNet
mv split/* UaNet_2D/src/split/

• Start training

cd UaNet_2D/src
python train.py