PKU-BDBA/BioAge

Multimodal Bioage Prediction

This repository contains the implementation of a multimodal image classification model that uses Swin Transformers for feature extraction from different types of images (e.g., face, tongue, and fundus) and a cross-attention mechanism to integrate features for final classification.

Structure

The code is organized as follows:

• dataset.py: Contains the MultiModalDataset class that handles loading and transforming the images.
• model/swin_transformer.py: Includes the SwinTransformerBlock and SwinTransformerEncoder classes for the Swin Transformer model (note: you need to implement the actual transformer block).
• model/cross_attention.py: Contains the CrossAttentionModule class for the cross-attention mechanism.
• model/classifier.py: Contains the MLPHeader and ClassifierModel classes for the MLP headers and the final classification model.
• main.py: The main script to run the training and evaluation of the model.

Installation

To install the necessary dependencies, run the following command:

pip install -r requirements.txt

Usage

To train the model, you need to set up your dataset by specifying the image paths and labels. Then run:

python main.py

This will start the training process and save the model after training. You can also use the provided code to perform validation and make predictions.

License

This project is licensed under the Apache-2.0 license - see the LICENSE file for details.