Giada Minghini

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About

Giada Minghini is a researcher at the forefront of applying high-performance hardware acceleration to medical imaging, with a specific focus on cardiovascular diagnostics. Her work centers on the intersection of deep learning, semantic segmentation, and field-programmable gate arrays (FPGAs) to enable real-time analysis of medical scans. Minghini’s major contribution lies in the segmentation of aortic valve calcium lesions—a critical task for assessing heart disease—using FPGA-based accelerators. Her 2025 paper on this topic, which has already garnered 2 citations, demonstrates how convolutional neural networks (CNNs) can be deployed on FPGA platforms via the Vitis AI toolchain to achieve efficient, hardware-optimized inference. A subsequent benchmark study further validates this approach, highlighting its potential for clinical deployment where speed and energy efficiency are paramount. By bridging the gap between advanced neural network architectures and custom hardware, Minghini is paving the way for more responsive, point-of-care diagnostic tools. Her work is particularly notable for its practical focus on accelerating computer vision tasks in medicine, offering a scalable solution that could transform how clinicians detect and quantify cardiac calcifications.