Github repo to run classification on uterine images.

To install the project, simply clone the repository and get the necessary dependencies:

git clone https://github.com/MarcoParola/uterine-classification.git
cd uterine-classification

Create the virtualenv (you can also use conda) and install the dependencies of requirements.txt

python -m venv env
. env/bin/activate
python -m pip install -r requirements.txt
mkdir data

Then you can download the uterine dataset.json (both images and json file) from TODO-put-link. Copy them into data folder and unzip the file gyn1.zip. Then, prepare the data as described here

Next, create a new project on Weights & Biases named uterine-classification. Edit entity parameter in config.yaml by sett. Log in and paste your API key when prompted.

wandb login 

Here is a quick overview of the main use of the repo. Further information is available in the official doc.

Classification on the whole dataset:

• Train CNN classifier on the whole dataset
• Test CNN classifier on the whole dataset

Specify the pre-trained classification model by setting model.weights. classification_mode=whole specifies we are solving the classification without exploiting the segment information.

# TRAIN classifier on whole images
python train.py task=c classification_mode=whole model.weights=ConvNeXt_Small_Weights.DEFAULT 

# TEST classifier whole images
python test.py task=c classification_mode=whole checkpoint.version=123

Classification on the masked dataset:

• Train CNN for segmentation
• test CNN for segmentation
• train CNN classifier on the masked dataset
• test CNN classifier on the masked dataset

Specify the pre-trained segmentation model by setting model_seg. classification_mode=masked specifies we are solving the classification by exploiting the segment information.

The first step of this task is to train a segmentation NN that will be used to generate masks for images in the next step.

# TRAIN segmentation NN
python train.py task=s model_seg='fcn'

# TEST segmentation NN
python test.py task=s model_seg='fcn' checkpoint.version=123

After training your segmentation NN insert the version of the model you want to exploit in the masked classification in the __init__ method of src/data/masked_classification/dataset.py. Specify the pre-trained classification model by setting model.weights. Specify the segmentation model previously trained for generate the masks by setting model_seg.

# TRAIN classifier on masked images
python train.py task=c classification_mode=masked model.weights=ConvNeXt_Small_Weights.DEFAULT model_seg='fcn' sgm_type='soft'

# TEST classifier on masked images
python test.py task=c classification_mode=masked model_seg='fcn' checkpoint.version=123

Classification on the whole dataset exploiting saliency maps and masks:

• train CNN classifier on the original dataset with backpropagating saliency map error
• test CNN classifier on the whole dataset

Specify the pre-trained classification model by setting model.weights. classification_mode=saliency specifies we are solving the classification by exploiting the saliency map information.

# TRAIN classifier on whole images with saliency map information
python train.py task=c classification_mode=saliency model.weights=ConvNeXt_Small_Weights.DEFAULT 

# TEST classifier on whole images with saliency map information
python test.py task=c classification_mode=saliency checkpoint.version=123

TODO: meglio passare a wandb (?)

python -m tensorboard.main --logdir=logs