Breast cancer tumour detection on mammograms

Requirements

GCC >= 4.9
CUDA 9.0 & cuDNN 7.0 (install. instructions)
Anaconda 3 (install. instructions)

References

Faster-RCNN paper: arxiv.org/pdf/1506.01497.pdf
Faster-RCNN implem. repo.: github.com/facebookresearch/maskrcnn-benchmark
RetinaNet paper: arxiv.org/pdf/1708.02002.pdf
RetinaNet implem. repo.: github.com/fizyr/keras-retinanet
FCOS paper: arxiv.org/pdf/1904.01355.pdf
FCOS implem. repo.: github.com/tianzhi0549/FCOS

Installation instructions

Start by cloning this repo:

git clone https://github.com/delmalih/MIAS-mammography-obj-detection

1. Faster R-CNN instructions

First, create an environment :

conda create --name faster-r-cnn
conda activate faster-r-cnn
conda install ipython pip
cd MIAS-mammography-obj-detection
pip install -r requirements.txt
cd ..

Then, run these commands (ignore if you have already done the FCOS installation) :

# install pytorch
conda install pytorch==1.1.0 torchvision==0.3.0 cudatoolkit=9.0 -c pytorch

export INSTALL_DIR=$PWD

# install pycocotools
cd $INSTALL_DIR
git clone https://github.com/cocodataset/cocoapi.git
cd cocoapi/PythonAPI
python setup.py build_ext install

# install cityscapesScripts
cd $INSTALL_DIR
git clone https://github.com/mcordts/cityscapesScripts.git
cd cityscapesScripts/
python setup.py build_ext install

# install apex
cd $INSTALL_DIR
git clone https://github.com/NVIDIA/apex.git
cd apex
python setup.py install --cuda_ext --cpp_ext

# install PyTorch Detection
cd $INSTALL_DIR
git clone https://github.com/facebookresearch/maskrcnn-benchmark.git
cd maskrcnn-benchmark
python setup.py build develop

cd $INSTALL_DIR
unset INSTALL_DIR

2. RetinaNet instructions

First, create an environment :

conda create --name retinanet python=3.6
conda activate retinanet
conda install ipython pip
cd MIAS-mammography-obj-detection
pip install -r requirements.txt
cd ..
pip install tensorflow-gpu==1.9
pip install keras==2.2.5

Then, run these commands :

# clone keras-retinanet repo
git clone https://github.com/fizyr/keras-retinanet
cd keras-retinanet
pip install .
python setup.py build_ext --inplace

Finally, replace the keras_retinanet/preprocessing/coco.py file by this file

3. FCOS instructions

First, create an environment :

conda create --name fcos
conda activate fcos
conda install ipython pip
cd MIAS-mammography-obj-detection
pip install -r requirements.txt
cd ..

Then, follow these instructions

How it works

1. Download the MIAS Database

Run these commands to download to MIAS database :

mkdir mias-db && cd mias-db
wget http://peipa.essex.ac.uk/pix/mias/all-mias.tar.gz
tar -zxvf all-mias.tar.gz
rm all-mias.tar.gz && cd ..

And replace the mias-db/Info.txt by this one

2. Generate COCO or VOC augmented data

It is possible to generate COCO or VOC annotations from raw data (all-mias folder + Info.txt annotations file) through 2 scripts: generate_{COCO|VOC}_annotations.py :

python generate_{COCO|VOC}_annotations.py --images (or -i) <Path to the images folder> \
                                          --annotations (or -a) <Path to the .txt annotations file> \
                                          --output (or -o) <Path to output folder> \
                                          --aug_fact <Data augmentation factor> \
                                          --train_val_split <Percetange of the train folder (default 0.9)>

For example, to generate 10x augmented COCO annotations, run this command :

python generate_COCO_annotations.py --images ../mias-db/ \
                                    --annotations ../mias-db/Info.txt \
                                    --output ../mias-db/COCO \
                                    --aug_fact 20 \
                                    --train_val_split 0.9

3. How to run a training

3.1 Faster R-CNN

To run a training with the Faster-RCNN:

Go to the faster-r-cnn directory: cd faster-r-cnn
Change conda env: conda deactivate && conda activate faster-r-cnn
Download the Resnet_101_FPN model
Trim the model: python trim_detectron_model.py --pretrained_path e2e_faster_rcnn_R_101_FPN_1x.pth --save_path base_model.pth

Edit the maskrcnn-benchmark/maskrcnn_benchmark/config/paths_catalog.py file and put these lines in the DATASETS dictionary :

DATASETS = {
...,
"mias_train_cocostyle": {
    "img_dir": "<PATH_TO_'mias-db'_folder>/<COCO_FOLDER>/images/train",
    "ann_file": "<PATH_TO_'mias-db'_folder>/<COCO_FOLDER>/annotations/instances_train.json"
},
"mias_val_cocostyle": {
    "img_dir": "<PATH_TO_'mias-db'_folder>/<COCO_FOLDER>/images/val",
    "ann_file": "<PATH_TO_'mias-db'_folder>/<COCO_FOLDER>/annotations/instances_val.json"
},
}

In the maskrcnn-benchmark/maskrcnn_benchmark/data/datasets/coco.py, comment line 84 to 92 :

# if anno and "segmentation" in anno[0]:
#     masks = [obj["segmentation"] for obj in anno]
#     masks = SegmentationMask(masks, img.size, mode='poly')
#     target.add_field("masks", masks)

# if anno and "keypoints" in anno[0]:
#     keypoints = [obj["keypoints"] for obj in anno]
#     keypoints = PersonKeypoints(keypoints, img.size)
#     target.add_field("keypoints", keypoints)

Run this command :

python train.py --config-file mias_config.yml

3.2 RetinaNet

To run a training with the retinanet :

cd retinanet
conda deactivate && conda activate retinanet
python train.py --compute-val-loss \ # Computer val loss or not
                --tensorboard-dir <Path to the tensorboard directory> \
                --batch-size <Batch size> \
                --epochs <Nb of epochs> \
                coco <Path to the COCO dataset>

And if you want to see the tensorboard, run on another window :

tensorboard --logdir <Path to the tensorboard directory>

3.3 FCOS

To run a training with the FCOS Object Detector :

Follow these instructions
Run this command :

cd fcos
conda deactivate && conda activate fcos
python train.py --config-file <Path to the config file> \
                OUTPUT_DIR <Path to the output dir for the logs>

4. How to run an inference

4.1 Faster R-CNN

To run an inference, you need a pre-trained model. Run this command:

cd faster-r-cnn
conda deactivate && conda activate faster-r-cnn
python inference.py --config-file <Path to the config file> \
                    MODEL.WEIGHT <Path to weights of the model to load> \
                    TEST.IMS_PER_BATCH <Nb of images per batch>

4.2 RetinaNet

Put the images you want to run an inference on, in <Name of COCO dataset>/<Name of folder>
Run this command :

cd retinanet
conda deactivate && conda activate retinanet
python inference.py --snapshot <Path of the model snapshot> \
                    --set_name <Name of the inference folder in the COCO dataset> \
                    coco <Path to the COCO dataset>

4.3 FCOS

cd fcos
conda deactivate && conda activate fcos
python inference.py --config-file <Path to the config file> \
                    MODEL.WEIGHT <Path to weights of the model to load> \
                    TEST.IMS_PER_BATCH <Nb of images per batch>

Results

Metric	Faster-RCNN	RetinaNet	FCOS
mAP	98,70%	94,97%	98,20%
Precision	94,12%	100,00%	94,44%
Recall	98,65%	94,72%	98,20%
F1-score	96,22%	96,93%	96,25%

Poster : Breast Cancer Detection Contest

delmalih / MIAS-mammography-obj-detection

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