Charlie Budd

King's College - North Carolina, King's College London

Papers

Total Citations

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About

Charlie Budd is a researcher at the forefront of computer-assisted surgery, specializing in medical image analysis, deep learning, and intraoperative imaging. His major contributions center on advancing surgical instrument segmentation—a critical technology for enhancing laparoscopic and robotic procedures. Budd developed SegMatch, a pioneering semi-semi-supervised learning method that dramatically reduces the need for expensive manual annotations while achieving high segmentation accuracy. This work, published in 2023 and 2025, has already garnered over 10 citations, reflecting its growing influence. He also led the creation of CholecInstanceSeg, a comprehensive tool instance segmentation dataset for laparoscopic surgery, which has quickly become a key resource in the field with 12 citations since its 2025 release. Additionally, Budd has explored novel applications of deep reinforcement learning for intraoperative hyperspectral video autofocusing, addressing critical hardware limitations in real-time tissue differentiation. His research bridges the gap between advanced AI techniques and practical surgical needs, offering scalable solutions for safer, more precise interventions. With a portfolio of highly cited, application-driven work, Charlie Budd is shaping the future of intelligent surgical systems.