
An advanced AI-assisted radiology workflow system designed to streamline and enhance the medical imaging interpretation process by combining state-of-the-art medical image segmentation with vision-language report generation capabilities. The platform intelligently analyzes radiological scans such as CT, MRI, X-ray, and ultrasound images, automatically identifying and segmenting clinically relevant anatomical structures, lesions, abnormalities, and regions of interest with high precision. Leveraging fine-tuned deep learning models specialized for medical imaging tasks, the system generates structured draft radiology reports that summarize findings, impressions, and potential diagnostic indicators in natural clinical language, significantly reducing reporting turnaround time while supporting physician productivity. The solution includes support for ROCm (Radeon Open Compute), enabling efficient deployment and acceleration on AMD GPU infrastructure. ROCm compatibility provides organizations with greater hardware flexibility, cost efficiency, and scalable performance for training and inference workloads in medical AI environments. This makes the system suitable for institutions seeking high-performance AI capabilities while leveraging AMD-based compute ecosystems in both on-premise and cloud deployments. Additional capabilities may include DICOM integration, PACS interoperability, configurable reporting templates, multimodal clinical context incorporation, automated prioritization workflows, segmentation visualization overlays, and support for human-in-the-loop validation pipelines. By reducing repetitive reporting tasks and accelerating preliminary analysis, the platform helps radiologists focus more on diagnostic decision-making, quality assurance, and patient care while maintaining clinical oversight and regulatory compliance.
10 May 2026

This comprehensive blockchain-based project showcases two interconnected smart contracts designed to demonstrate fundamental concepts in decentralized finance (DeFi) and non-fungible token (NFT) management on the Arc testnet. The project serves as an educational example for developers looking to understand smart contract development patterns, security considerations, and practical applications of blockchain technology in financial and digital asset systems. The bank contract provides a foundational framework for handling cryptocurrency transactions, while the NFT shop contract demonstrates how to create, manage, and interact with digital collectibles in a blockchain environment. Both contracts are built with security best practices in mind, utilizing modern Solidity features and following established patterns for smart contract development. The integration between these two contracts illustrates how different blockchain applications can work together to create more complex and valuable decentralized systems, making this project a valuable learning resource for blockchain developers and enthusiasts.
24 Jan 2026