A selection of projects I've built and contributed to.
LiverLens is a neuro-symbolic multimodal pipeline designed for automated liver metabolic health profiling from 3D CT scans while preventing AI hallucination of clinical metrics. The system separates perception, computation, and reporting into specialized stages so that neural models handle pattern recognition and language, while deterministic code handles quantitative analysis.
Apple's SHARP model is an advanced AI technology that can generate photorealistic 3D scenes from a single 2D image in under a second, utilizing a 3D Gaussian representation. In this project we will be using that.
A deep learning application that automatically segments the liver from 3D CT scans, comparing two architectures: SwinUNETR (Vision Transformer) - State-of-the-art attention-based model UNet (CNN) - Traditional convolutional baseline
This was my first experience working with a large-scale 3D medical imaging dataset. The MSD Task03 Liver dataset is over 25GB, and training a full 3D model required designing a strong backend pipeline for data preprocessing, batching, and sliding-window inference. Training the model for ~160 epochs on my local RTX 3060 (12GB), and the system achieved a 95.6% Dice score. SwinUNETR captures long-range anatomical dependencies better than CNNs. What the system does: 1. Reads and processes full 3D abdominal CT volumes 2. Standardizes orientation (RAS), voxel spacing, intensity ranges, and crops foreground 3. Uses a 3D Vision Transformer (SwinUNETR) for segmentation 4. Handles inference using MONAI’s sliding-window engine and produces accurate 3D liver masks that can be downloaded as NIfTI files What I built: 1. A fully modular preprocessing pipeline (orientation, cropping, resizing) 2. An optimized training loop with Dice loss, mixed precision, checkpointing, and metric tracking 3. A complete evaluation script for volume-wise Dice scoring 4. A trained SwinUNETR model that generalizes well on held-out test CT volumes
The platform enables users to generate legally valid digital agreements, discover relevant government welfare schemes, and analyze complex legal documents using natural language processing.
A multi-agent AI platform that delivers business intelligence across recruitment, market analysis, and lead generation