Code for "Transfer Learning without Knowing: Reprogramming Black-box Machine Learning Models with Scarce Data and Limited Resources". (ICML 2020)

"Transformer-based end-to-end classification of variable-length volumetric data" that will appear in MICCAI 2023.

Open-source glaucoma detection AI for mobile/low-resource clinics using synthetic training data

An elegant macOS demo app built with SwiftUI that leverages CoreML for real-time bone fracture detection from X-ray images. This project demonstrates how deep learning models can be seamlessly integrated into Swift-based apps for medical image analysis and AI-powered diagnostics.

AI powered Image-Based application for detecting and predicting disease and abnormalities.

ImFlow: A better image dataset loader for TensorFlow

PyTorch implementation of ConvNeXt for Alzheimer's MRI classification (80.9% Accuracy).

Hybrid ML pipeline using FCM segmentation, EfficientNetB4 feature extraction, and XGBoost for leukemia detection.

Deep Learning for Alzheimer’s Detection: Classifying anatomical MRI scans using advanced neural networks.

This project culminates in a model that can classify a given chest x-ray for the presence or absence of pneumonia.

Classifies MRI scans into Non-Demented, Very Mild, Mild, and Moderate Dementia using a fine-tuned VGG16 model. Implements data augmentation, class weighting, and Grad-CAM for interpretability.