This includes the code for different Skin Lesion Classification models to classify the 7 different types of skin lesions using the HAM10000 dataset.

The code was developed partly for the Advanced Machine Learning (2023-2024) and for the Computer Vision (2023-2024) course in the Master Program in Computer Science at the University of Rome "La Sapienza".

The code includes a re-implementation of the MSLANet - Multi-Scale Long Attention Network for Skin Classification paper, with some adjustments in order to enhance the performances, along with other solutions using Semantic Segmentation with Segment Anything Model and classification with CNNs and Vision Transformers.

The model also takes some parts of the code from the Pytorch StyleGAN, implementation, and from Image2StyleGAN and Image2StyleGAN++ papers unofficial implementations to generate synthetic images as data augmentation.

The work was carried out by:

• Domiziano Scarcelli
• Alessio Lucciola
• Danilo Corsi

We use Python 3.10.11 which is the last version supported by PyTorch. To create the environment using conda do

conda env create -f environment.yaml
conda activate aml_project

You can download the needed data from this Google Drive Link

Inside the data folder, there should be these elements:

• HAM10000_images_train: the directory containing the train images (The original name of the folder is HAM10000_images_part_1 and HAM10000_images_part_2)
• HAM10000_segmentations_lesion_tschandl: the directory containing the masks for the images
• HAM10000_metadata_train.csv: the .csv file containing the metadata for the train images (labels and other info). (The original name of the file is HAM10000_metadata)
• HAM10000_augmented_images: The directory containing the GAN-generated images to augment the training set
• synthetic_metadata_train.csv: The .csv file containing the metadata for the GAN generated images.

Moreover, to use SAM, it is necessary to put the sam_checkpoints.pt (Download at this Google Drive Link) file inside the checkpoints folder.

To train a model:

• Change the configurations you wish to use in the config.py file
• To train the MSLANet model, run python -m train_loops.MSLANet. To train a CNN, run python -m train_loops.CNN_pretrained. To train a ViT, run python -m train_loops.ViT.
• If you want to resume the training of a model, set RESUME=True in the config file, select the folder in which the model is and the checkpoint model. Then start the training again with the command above.

To test a model go to train_loops.test_loop and select the model to test (checkpoint number or "best"). Then start python -m train_loops.test_loop.

The plotting function can be found in the plots folder. Ensure you have the tr_val_results.json and test_results.json for a specific model in the results folder. Select the folder name of the model(s) of which to plot the results, then run python -m plots.test_plots and python -m plots.train_plots.

To run the demo, execute python -m demo.demo_app. Notice that it is necessary to choose the model to use in the demo.demo_app file (the process is the same as the one used in the testing phase).