Overview 🚀 This repository contains an implementation of Vision Transformers (ViTs) specifically designed for processing medical images. The model leverages Locally-Grouped Self-Attention, or LSA and Shift Packet Tokenization to enhance its performance on medical imaging datasets. Vision Transformers have shown remarkable success in computer vision tasks, and this project aims to adapt and optimize them for the unique characteristics of medical images.
Clone it! and happy coding!
Features 🌟
Vision Transformer Architecture: The core of the model is based on the Vision Transformer architecture, which has demonstrated state-of-the-art performance on various visual recognition tasks.
Locally-Grouped Self-Attention, or LSA: LSA is incorporated to capture latent patterns and relationships within the medical images, enabling the model to learn more robust representations.
Shift Packet Tokenization: A novel tokenization technique, called Shift Packet Tokenization, is implemented to handle the specific challenges posed by medical image data. This technique helps the model focus on relevant image features.
Preprocessing Pipeline: The repository includes a comprehensive preprocessing pipeline tailored for medical images, ensuring that the data is appropriately prepared for input into the Vision Transformer.
Training and Evaluation Scripts: The code includes training and evaluation scripts that facilitate the training of the model on medical image datasets and the assessment of its performance.
Requirements 📋 Python 3.x PyTorch NumPy Scikit-learn Your preferred deep learning environment (e.g., CUDA for GPU acceleration)# vision-Transformers-for-medical-images