Hierarchical, informed and robust machine learning for surgical tool management

This thesis focuses on the development of a computer vision and deep learning based system for the intelligent management of surgical tools. The work accomplished included the development of a new dataset, creation of state of the art techniques to cope with volume, variety and vision problems, and designing or adapting algorithms to address specific surgical tool recognition issues. The system was trained to cope with a wide variety of tools, with very subtle differences in shapes, and was designed to work with high volumes, as well as varying illuminations and backgrounds. Methodology that was adopted in this thesis included the creation of a surgical tool image dataset and development of a surgical tool attribute matrix or knowledge-base. This was significant because there are no large scale publicly available surgical tool datasets, nor are there established annotations or datasets of textual descriptions of surgical tools that can be used for machine learning. The work resulted in the development of a new hierarchical architecture for multi-level predictions at surgical speciality, pack, set and tool level. Additional work evaluated the use of synthetic data to improve robustness of the CNN, and the infusion of knowledge to improve predictive performance.

Grantor dc:publisher.institution

The University of Waikato

Year dc:date.issued

2023

Author dc:creator

• Rodrigues, Mark William

Advisors dc:contributor.advisor

• Smith, Tony C.
• Mayo, Michael

• Machine learning
• Surgical tools
• Computer vision
• Hospi-tools dataset
• OctopusNet

Statement dc:rights

• All items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.

Licence

• https://researchcommons.waikato.ac.nz/bitstreams/22bdde69-a3ce-45fb-afe0-f1687f77bac9/download