Erfan Salehi.

Cardiovascular diseases are the leading cause of death globally. Early diagnosis via ECG is critical but requires specialized expertise. I built an end-to-end deep learning pipeline to classify ECG signals into 5 diagnostic superclasses: Normal, Myocardial Infarction, ST/T Change, Conduction Disturbance, and Hypertrophy.

• SE-ResNet architecture with Squeeze-and-Excitation blocks for lead-wise attention across all 12 ECG leads
• Custom data pipeline with bandpass filtering, Z-score normalization, and stratified splitting on PTB-XL (21,799 records)
• Mixup augmentation to generate synthetic training samples and Focal Loss to address heavy class imbalance
• Ensemble of baseline + Focal Loss variants for state-of-the-art multi-label performance
• External validation on Chapman-Shaoxing dataset (10,646 records) to test generalization

This project sits at the intersection of AI and real clinical impact. The ensemble approach achieved 0.932 Macro-AUC on the PTB-XL test set, with strong external generalization (0.844 AUROC). It demonstrates how thoughtful architecture choices and training techniques can meaningfully close the gap between AI models and clinical-grade tools.

Manual review of cervical cytology images is time-consuming and subjective. Automating the classification of cells into normal, dysplastic, and malignant categories can significantly improve the efficiency and consistency of pap smear screenings.

• End-to-end data pipeline for the SIPaKMeD dataset including preprocessing and augmentation.
• Deep learning classification model using ResNet/EfficientNet backbones for 8-class categorization.
• Explainable AI (XAI) integration utilizing Grad-CAM to highlight regions of interest for clinical transparency.
• Production-ready REST API built with FastAPI, supporting batch processing and real-time inference.
• Dockerized deployment setup with GPU acceleration support for scalable production use.