John Plemmons
Papers
2
Total Citations
16
H-Index
2
About
John Plemmons is a researcher at the forefront of applying deep learning to medical imaging, with a primary focus on breast cancer diagnosis and robotic surgery planning. His major contributions center on developing advanced segmentation architectures for multi-modality breast MRI, leveraging the state-of-the-art nnU-Net framework to precisely delineate thoracic regions and breast tissues. This work is critical for both analyzing breast masses and creating tissue-delineating phantoms that guide robotic tumor surgery navigation. His most cited paper (14 citations) introduces a novel aggregation of two neural networks for this purpose, while a subsequent study (2 citations) establishes the foundational cascaded DNN architecture for preoperative planning. Though his citation counts are still growing, Plemmons’ research directly addresses a pressing clinical need: improving the accuracy and safety of robotic surgery through superior image segmentation. His work represents a vital step toward integrating AI-driven preoperative planning into real-world surgical workflows, making him a promising voice in the intersection of computer vision, medical robotics, and oncology.
Research Focus
Key Achievements
Top Papers
- 1
- 2