xuan139 / cdt-api-network

CDT-API-NETWORK

This repository contains the dataset and model code used in Attentive Pairwise Interaction Network for AI-assisted Clock Drawing Test Assessment of Early Visuospatial Deficits. In clock_shulman.zip, internal folder is splited by shulman clock score.

Introduction

Dementia is a debilitating neurological condition which impairs the cognitive function and the ability to take care of oneself. The Clock Drawing Test (CDT) is a simple and well-known test for detecting dementia. While clear abnormalities in CDT indicate high risk of dementia, discriminating between normal versus borderline cases requires years of clinical experience. Misclassifying mild abnormal as normal will delay the chance to investigate for potential reversible causes or slow down the progression. To help address this issue, we propose an automatic CDT scoring system that adopts Attention Pairwise Interaction Network (API-Net), a fine-grained deep learning model that is designed to detect subtle differences in images. Inspired by how humans often learn to recognize different objects by looking at two images side-by-side, the API-Net learns to detect the subtle differences by comparing image pairs contrastively.

How to use

** The provided training script is just an example for creating training pipeline.

Create Conda Environment: conda env create -f environment.yml

Then run: python train.py --data ./clock_shulman/clock_shulman

Model performance

Model	Accuracy	F1-Score	Precision	Recall
ResNet-152	0.7668±0.0074	0.7581±0.0079	0.7654±0.0080	0.7668±0.0074
API-Net (ResNet-152)	0.7802±0.0030	0.7653±0.0084	0.7727±0.0057	0.7743±0.0054
API-Net (ResNet-121) with gradual unfreezing	0.7892±0.0104	0.7964±0.0120	0.7871±0.0108	0.7892±0.0104

Contact:

Please feel free to contact [email protected] or [email protected], if you have any questions.