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About

Boheng Zhang is a rising researcher at the forefront of medical robotics and AI-driven ultrasound imaging, whose work directly addresses critical challenges in clinical diagnostics and infectious disease management. His primary research areas encompass medical image segmentation, robotic autonomous scanning, and deep learning-based target detection. Zhang’s major contributions are exemplified by his development of MM-UKAN++, a novel Kolmogorov–Arnold Network-based U-shaped architecture that achieves state-of-the-art performance in ultrasound image segmentation—a task notoriously difficult due to low contrast and noise. This work, published in 2025, has already garnered 15 citations, signaling its rapid impact. He further advanced the field by creating a visual perception and convolutional neural network-based system for robotic autonomous lung ultrasound scanning, earning 10 citations for its potential to reduce clinician infection risk during the COVID-19 pandemic. Zhang also proposed a channel and spatial attention mechanism-enhanced YOLO network for precise target detection in lung ultrasound scanning robots. His integrated approach—combining novel network architectures with practical robotic systems—positions him as a key innovator in making ultrasound diagnosis safer, faster, and more accessible.