Coherent Concept-based Explanations in Medical Image and Its Application to for Skin Lesion Diagnosis

Paper accepted at the CVPR 2023 workshop SAIAD - Safe Artificial Intelligence for All Domains.

Citation

If you use this repository, please cite:

@inproceedings{patricio2023coherent,
  title={Coherent Concept-based Explanations in Medical Image and Its Application to Skin Lesion Diagnosis},
  author={Patr{\'\i}cio, Cristiano and Neves, Jo{\~a}o C and Teixeira, Luis F},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  pages={3798--3807},
  year={2023}
}

1. Dataset Statistics

Dataset	Train		Validation		Test		Total
	Mel.	Nev.	Mel.	Nev.	Mel.	Nev.
PH2	30	120	5	20	5	20	200
Derm7pt	90	256	61	100	101	219	827
PH2D7	120	376	66	120	106	239	1,027

2. Reproducibility of the results

Download the required datasets:

1. PH2: https://www.fc.up.pt/addi/ph2%20database.html
2. Derm7pt: https://derm.cs.sfu.ca/Welcome.html

Note: To obtain the preprocessed images of each dataset, please e-mail me at cristiano [dot] patricio [at] ubi [dot] pt.

First of all, create a new conda environment with the required libraries contained in requirements.txt file:

conda create --name <env> --file requirements.txt

For evaluating the model in a specified dataset, please ensure that you modify the directory paths and specify the parameters in model_params.py

Evaluate a baseline model:

In model_params.py set BASELINE = True

In evaluate.py choose the model: models = ["resnet101"] # "densenet201", "seresnext" and uncomment baseline gammas:

# BASELINE
gammas = {"ph2": [None, None, None],
          "ph2_dlv3_ft": [None, None, None],
          "ph2_manually": [None, None, None],
          "derm7pt": [None, None, None],
          "derm7pt_dlv3_ft": [None, None, None],
          "derm7pt_manually": [None, None, None],
          "ph2derm7pt": [None, None, None],
          "ph2derm7pt_dlv3_ft": [None, None, None],
          "ph2derm7pt_manually": [None, None, None]}

and finally, run the script:

python evaluate.py

Evaluate the proposed model:

In model_params.py set BASELINE = False

In evaluate.py choose the model: models = ["resnet101"] # "densenet201", "seresnext" and uncomment OUR METHOD gammas:

# OUR METHOD
gammas = {"ph2": [0.6, 0.6, 0.6],
          "ph2_dlv3_ft": [0.6, 0.6, 0.6],
          "ph2_manually": [0.6, 0.6, 0.6],
          "derm7pt": [0.3, 0.7, 0.3],
          "derm7pt_dlv3_ft": [0.6, 0.5, 0.6],
          "derm7pt_manually": [0.6, 0.5, 0.5],
          "ph2derm7pt": [0.4, 0.9, 0.6],
          "ph2derm7pt_dlv3_ft": [0.4, 0.7, 0.6],
          "ph2derm7pt_manually": [0.4, 0.7, 0.6]}

and finally, run the script:

python evaluate.py

A TXT file will be created with the results at results/.

2. Optimal Gamma Values

Dataset	ResNet-101	DenseNet-201	SEResNeXt
PH2	0.6	0.6	0.6
Derm7pt	0.3	0.7	0.3
PH2Derm7pt	0.4	0.9	0.6
PH2_DLV3	0.6	0.6	0.6
Derm7pt_DLV3	0.6	0.5	0.6
PH2Derm7pt_DLV3	0.4	0.7	0.6
PH2_Manually	0.6	0.6	0.6
Derm7pt_Manually	0.6	0.5	0.5
PH2Derm7pt_Manually	0.4	0.7	0.6

3. Training a model

For training the model in a specified dataset, please ensure that you modify the directory paths and specify the parameters in model_params.py

Training a baseline model:

In model_params.py set BASELINE = True

In model_training.py choose the model: models = ["resnet101"] # "densenet201", "seresnext" and uncomment baseline gammas:

# BASELINE
gammas = {"ph2": [None, None, None],
          "ph2_dlv3_ft": [None, None, None],
          "ph2_manually": [None, None, None],
          "derm7pt": [None, None, None],
          "derm7pt_dlv3_ft": [None, None, None],
          "derm7pt_manually": [None, None, None],
          "ph2derm7pt": [None, None, None],
          "ph2derm7pt_dlv3_ft": [None, None, None],
          "ph2derm7pt_manually": [None, None, None]}

and finally, run the script:

python model_training.py

Training the proposed model:

In model_params.py set BASELINE = False

In model_training.py choose the model: models = ["resnet101"] # "densenet201", "seresnext" and uncomment OURS gammas:

# OURS
gammas = {"ph2": [0.6, 0.6, 0.6],
          "ph2_dlv3_ft": [0.6, 0.6, 0.6],
          "ph2_manually": [0.6, 0.6, 0.6],
          "derm7pt": [0.3, 0.7, 0.3],
          "derm7pt_dlv3_ft": [0.6, 0.5, 0.6],
          "derm7pt_manually": [0.6, 0.5, 0.5],
          "ph2derm7pt": [0.4, 0.9, 0.6],
          "ph2derm7pt_dlv3_ft": [0.4, 0.7, 0.6],
          "ph2derm7pt_manually": [0.4, 0.7, 0.6]}

and finally, run the script:

python model_training.py

4. Plot the results

In evaluate.py set the plot_results parameter to True.