DLMI_COVID19_Radiography

This repository is NTU Deep Learning for Medical Imaging course 2024 final project. It contains models and methodologies for classifying and segmenting chest X-ray images into categories such as COVID-19, Lung Opacity, Normal, and Viral Pneumonia. The models also perform multi-task learning to handle both classification and segmentation in a unified framework.

Data

The data used in this project is the COVID-19 Chest X-Ray Database available on Kaggle. It includes 21,165 images with corresponding lung masks, categorized into:

• COVID-19: 3,616 images
• Lung Opacity: 6,012 images
• Normal: 10,192 images
• Viral Pneumonia: 1,345 images

Each image and mask is provided at a resolution of 300x300 pixels in PNG format.

Project Objective

The project's goal is to leverage advanced machine learning techniques to enhance the accuracy and efficiency of diagnosing chest-related diseases from X-ray images. This involves:

• Classification: Using supervised, self-supervised, and zero-shot methods.
• Segmentation: Employing various supervised segmentation models.
• Multi-Task Learning: Integrating classification and segmentation tasks within a single model framework.

Methodology

Classification Techniques

1. Supervised Learning: Models like Swin Transformer, VIT Base, and others are fine-tuned using the complete training dataset.
2. Self-Supervised Learning: Implements models such as DINOv2 and BEITv2, which utilize partial dataset fine-tuning and frozen encoder layers to enhance training speed and reduce performance degradation.
3. Zero-Shot Learning: Utilizes the CLIP model with specifically designed prompts to classify images without direct training on the task.

Segmentation Models

Models such as Unet, Unet++, and DeepLabV3+ are used to segment the chest X-ray images, focusing on achieving high Dice scores and accurate lung mask segmentation.

Multi-Task Learning

The Unet architecture is modified to include a classification branch post-encoder to simultaneously perform classification and segmentation.

Experiments and Analysis

• Training Details: The models are trained with an 80/20 split for training and validation sets using Adam optimizer, a learning rate of 5e-5, and a weight decay of 1e-6.
• Performance Metrics: Models are evaluated based on F1 score, precision, recall, accuracy, and training speed.

References

• Kaggle COVID-19 Radiography Database
• torchvision Models