Medical Image Radiomics Processor

Medical imaging annotation processor with multi-format parsing (XML/JSON/PDF), automated keyword extraction, 3D contour analysis, FastAPI+React interface, Supabase integration, Excel/SQLite/PostgreSQL export, LIDC-IDRI support, radiologist agreement analytics, and batch processing up to 1000 files

Building an AI-Powered Medical Assessment System: A Multi-Agent Approach to Injury Triage

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Medical image viewer and processor

Use LLMs to Process Medical Documents

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NHS Medical Document Processor Framework for Cardiovascular Issues (NHS Personal Hobby Project/Framework - DPCI) - Modularized Demo (MVP) (Follows NHS DTAC guidelines + NHS AI Guidelines + NICE standards) (By Bhargav Ashok)

Image Processor Final Project for Medical Software Design

n8n workflow for processing medical documents with AI classification

HealthPay Claim Processor is an AI-powered FastAPI application designed to automate the ingestion, classification, extraction, and structuring of medical claim documents

Hawk-Net: Medical Image Segmentation and Classification Using Multi-scale Convolutional Self-Attention-Based Image Processor with DK-CNN-Mamba-xAttention Fusion Network

MRP 5G Session Processor ingests medical consultation recordings and automatically transcribes them, identifies speakers (doctor, patient, specialist), and organizes the conversation into structured clinical sections with concise summaries.

California Plug Load Research Center (CalPlug) This is the second version of Urology. This verson features two circuit boards and additional features from upgraded hardware such as the ESP 32 WROVER. The essential features of this device is a force sensor, battery management system with a guage, on board FTDI, LED indicators, throw switch, tare button, current sync with LED bar, piezo sound generator, 3V3 Regulator/Boost and WiFi/BLE. The reason we decided to use the ESP32 WROVER was to allow for an antenna extension to be plugged into the processor. Our case needed to be metal to follow medical equipment guidelines and this required us to have an external antenna. This project has two circuit boards. The top board is designated for all user interface controls in order to reduce wire connections to bottom board. The bottom board is the brains of the entire circuitry. We have made code upload possible through USB. In addition, charging the LiPo battery is possible through USB input. To keep connections clean, JST connectors were used connect boards. This is a team project with CalPlug researchers and my role was lead hardware designer. My contributions include block diagrams, Eagle Schematics, and the Bottom Board Layout.

This project illustrates the design and performance evaluation of few algorithms used for analysing the medical image volumes on the massive parallel graphics processing unit (GPU) with compute unified device architecture (CUDA). These algorithms are selected from the general framework, devised for computer aided diagnostic (CAD) system. The CAD system used for analysing large medical image datasets are usually a pipeline processing that includes a variety of image processing operations. A MRI scanner captures the 3D human head into a series of 2D images. Considerable time spent in pre and post processing of these images. Noise filters, segmentation, image diffusion and enhancement are few such methods. The algorithms are chosen for study requires local information, available in few pixels or global information available in the entire image. These problems are best candidates for GPU implementation, since the parallelism is naturally provided by the proposed Per-Pixel Threading (PPT) or Per-Slice Threading (PST) operations. In this paper implement the algorithms for adaptive filtering, anisotropic diffusion, bilateral filtering, non-local means (NLM) filtering, K-Means segmentation and feature extraction in 1536 core’s NVIDIA GPU and estimated the speed up gained. Our experiments show that the GPU based implementation achieved typical speedup values in the range of 3-338 times compared to conventional central processing unit (CPU) processor in PPT model and up to 30 times in PST model.

Medical Image Processor

Process Medical Record in PDF format to format that can be consummed by LLMs and AI apps.

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This project is used to identify patterns in medical text using Standford CoreNLP module

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A single-page web app for medical offices that manages patient data, generates printable forms, and uses AI to process handwritten documents with local storage and JSON export.

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Medical Image Processor and Repository

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FastAPI app to process medical claims