West-China-Hospital-RespAI

West China Respiratory Chronic Disease Series Research Code library / 华西呼吸慢病系列研究代码库

Preface

• Motivation
  Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Early and accurate detection is crucial for improving patient outcomes.

• Problem Statement
  We aim to explore optimal neural network architectures, fine-tune training parameters, ensure interpretability, and refine preprocessing methods—addressing COPD-specific challenges while balancing classification performance with clinical transparency.

• Approach
  This project compares multiple architectures (CNN variants, vision transformer networks, etc.) to determine the best-performing model for COPD classification. Interpretability techniques (e.g., Grad-CAM, Occlusion Sensitivity) are also employed to clarify how each model reaches its decisions.

Model Architectures

(a) ResNet configuration (b) DenseNet configuration (c) ViT configuration

Interpretability

CAM

Grad-CAM and Occlusion Sensitivity

Installation & Usage

1. Setup

git clone https://github.com/keyanshagua/West-China-Hospital-RespAI.git

pip install -r requirements.txt

2. Get the MONAI Docker Image

docker pull projectmonai/monai:latest