Detecting cardiovascular diseases using ECG scans and explainable artificial intelligence
An explainable AI framework for quantifying the stability of deep learning models analyzing ECG scans under controlled image perturbations.
Papers
My published research papers.
Interpretable arrhythmia detection in ECG scans using deep learning ensembles: a genetic programming approach
A deep learning ensemble framework for detecting cardiac arrhythmias and predicting atrial fibrillation recurrence from ECG scans.
Giraffe: A Genetic Programming Algorithm To Build Deep Learning Ensembles For Ecg Arrhythmia Classification
A genetic programming algorithm for building deep learning ensembles for ECG arrhythmia classification.
Automated Hepatocellular Carcinoma Analysis in Multi-phase CT with Deep Learning
A deep learning approach for automated hepatocellular carcinoma analysis in multi-phase CT scans.
Crafting Deep Learning Models for Classifying ECG Paper Printouts
An InceptionV3-based approach for classifying ECG paper printouts using the Generalized Extreme Value activation function and loss weighting, developed as part of the George B. Moody PhysioNet Challenge 2024.
Deep learning ensembles for detecting brain metastases in longitudinal multi-modal MRI studies
A deep learning approach for detecting brain metastases in longitudinal multi-modal MRI studies.
Detecting liver cirrhosis in computed tomography scans using clinically-inspired and radiomic features
A novel approach for detecting cirrhosis from CT scans using clinically-inspired and radiomic features.