EXACT-CT: EXplainable Analysis for Crohn's and Tuberculosis using CT
Journal:
arXiv
Published Date:
Feb 28, 2025
Abstract
Crohn's disease and intestinal tuberculosis share many overlapping features
such as clinical, radiological, endoscopic, and histological features -
particularly granulomas, making it challenging to clinically differentiate
them. Our research leverages 3D CTE scans, computer vision, and machine
learning to improve this differentiation to avoid harmful treatment
mismanagement such as unnecessary anti-tuberculosis therapy for Crohn's disease
or exacerbation of tuberculosis with immunosuppressants. Our study proposes a
novel method to identify radiologist - identified biomarkers such as VF to SF
ratio, necrosis, calcifications, comb sign and pulmonary TB to enhance
accuracy. We demonstrate the effectiveness by using different ML techniques on
the features extracted from these biomarkers, computing SHAP on XGBoost for
understanding feature importance towards predictions, and comparing against
SOTA methods such as pretrained ResNet and CTFoundation.
