Max-Heinrich Laves

Leibniz University Hannover, Universität Hamburg

Papers

Total Citations

127

H-Index

About

Max-Heinrich Laves is a leading researcher at the intersection of medical imaging, deep learning, and robotic intervention. His primary contributions lie in developing intelligent systems for minimally invasive diagnostics and safe tissue sampling. Laves is best known for his pioneering work on semantic segmentation of laryngeal endoscopic images, where his 2019 paper—cited over 108 times—established a benchmark dataset and convolutional neural network framework that has become foundational for automated analysis of the upper airway. This work has enabled more precise, AI-assisted diagnostics in otolaryngology. In response to the COVID-19 pandemic, Laves advanced robotic biopsy techniques to reduce disease transmission risks during post-mortem examinations. His 2022 study on robotic tissue sampling for infectious corpses (16 citations) introduced a novel approach to safely obtain histopathological and microbiological samples, directly informing treatment strategies. He further refined this with needle insertion planning algorithms that avoid critical anatomical obstacles, enhancing the precision and safety of robotic biopsy. Through these contributions, Laves has demonstrated how robotics and AI can transform pathology and legal medicine, making high-risk procedures safer and more reliable. His work continues to shape the future of autonomous medical systems.