ProCyon: A multimodal foundation model for protein phenotypes

BioContextAI Knowledgebase MCP server for biomedical agentic AI

The BioContextAI Registry for biomedical MCP servers

Cookiecutter template for MCP server development with FastMCP

This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

Biomedical Artificial Intelligence

🩺 AstraMed: A Clinical Risk Intelligence Platform. Powered by SOTA Ensemble ML & BioMistral-7B for predictive medical analytics and explainable risk scoring.

Lab website

Breast ultrasound image segmentation app using a U-Net with ResNet50V2 backbone, featuring an interactive Gradio demo and AI-based lesion detection.

Lightweight CNN with Attention for Heart Sound Classification

This project builds machine learning models to automatically detect and classify protein complexes in cryo-electron tomography (cryoET) images, enabling scalable analysis of cellular structures and supporting advanced biological and medical research.

🩺 RAGnosis — An AI-powered clinical reasoning assistant that retrieves real diagnostic notes (from MIMIC-IV-Ext-DiReCT) and generates explainable medical insights using Mistral-7B & FAISS, wrapped in a clean Gradio UI. ⚡ GPU-ready, explainable, and open-source.

Web-based semi-automated retinal fluid segmentation tool for OCT images. Combines traditional image processing with deep learning models (U-Net, MedSAM) to help medical professionals efficiently annotate and segment retinal fluid regions. Features interactive annotation tools, multiple segmentation algorithms, and user-friendly web interface.

Deep learning system that interprets brain signals from EEG/fMRI to control devices, predict neurological disorders, and enable brain-computer interfaces.

This repository contains a deep learning-based cancer type prediction system using a trained convolutional neural network (CNN). The model is deployed using Streamlit, allowing users to upload medical images and receive predictions with a probability distribution displayed in a pie chart.

Masters Thesis: Deep Learning Based Heart Sound Classification using Multimodal Features (PyTorch)

Face-aware adaptation of self-supervised motion magnification for micro-expression spotting on CASME II.

Improving diagnostic confidence in histopathology through fine-tuned vision models and interpretable slide-level inference tools.

Research archive on Deep Learning and CNN applications in automated radiographic assessment (2020)

Biomedical AI framework - multi-modal learning, drug discovery, genomic analysis, clinical knowledge graphs