In this section
The Cognitive Vision in Robotic Surgery Lab is developing computer vision and AI techniques for intraoperative navigation and real-time tissue characterisation.
Dr Stamatia (Matina) Giannarou
411 Bessemer Building
South Kensington Campus
+44 (0) 20 7594 8904
Surgery is undergoing rapid changes driven by recent technological advances and our on-going pursuit towards early intervention and personalised treatment. We are developing computer vision and Artificial Intelligence techniques for intraoperative navigation and real-time tissue characterisation during minimally invasive and robot-assisted operations to improve both the efficacy and safety of surgical procedures. Our work will revolutionize the treatment of cancers and pave the way for autonomous robot-assisted interventions.
With recent advances in medical imaging, sensing, and robotics, surgical oncology is entering a new era of early intervention, personalised treatment, and faster patient recovery. The main goal is to completely remove cancerous tissue while minimising damage to surrounding areas. However, achieving this can be challenging, often leading to imprecise surgeries, high re-excision rates, and reduced quality of life due to unintended injuries. Therefore, technologies that enhance cancer detection and enable more precise surgeries may improve patient outcomes.
Our methods aim to ensure patients receive accurate and timely surgical treatment while reducing surgeons' mental workload, overcoming limitations, and minimizing errors. By improving tumor excision, our hybrid diagnostic and therapeutic tools will lower recurrence rates and enhance survival outcomes. More complete tumor removal will also reduce the need for repeat procedures, improving patient quality of life, life expectancy, and benefiting society and the economy.
Dr Stamatia Giannarou
Senior Lecturer
Dr Po Wen Lo
Research Associate
Mr Alfie Roddan
Research Postgraduate
Mr Alistair G Weld
Research Postgraduate
Mr Chi Xu
Research Assistant
Mr Haozheng Xu
Research Assistant in Surgical Robot Vision
Mr Yihang Zhou
Research Assistant
@inproceedings{Roddan:2023:10.1007/978-3-031-43895-0_54,
author = {Roddan, A and Xu, C and Ajlouni, S and Kakaletri, I and Charalampaki, P and Giannarou, S},
doi = {10.1007/978-3-031-43895-0_54},
pages = {575--585},
publisher = {Springer Nature Switzerland},
title = {Explainable image classification with improved trustworthiness for tissue characterisation},
url = {http://dx.doi.org/10.1007/978-3-031-43895-0_54},
year = {2023}
}
TY - CPAPER
AB - The deployment of Machine Learning models intraoperatively for tissue characterisation can assist decision making and guide safe tumour resections. For the surgeon to trust the model, explainability of the generated predictions needs to be provided. For image classification models, pixel attribution (PA) and risk estimation are popular methods to infer explainability. However, the former method lacks trustworthiness while the latter can not provide visual explanation of the model’s attention. In this paper, we propose the first approach which incorporates risk estimation into a PA method for improved and more trustworthy image classification explainability. The proposed method iteratively applies a classification model with a PA method to create a volume of PA maps. We introduce a method to generate an enhanced PA map by estimating the expectation values of the pixel-wise distributions. In addition, the coefficient of variation (CV) is used to estimate pixel-wise risk of this enhanced PA map. Hence, the proposed method not only provides an improved PA map but also produces an estimation of risk on the output PA values. Performance evaluation on probe-based Confocal Laser Endomicroscopy (pCLE) data verifies that our improved explainability method outperforms the state-of-the-art.
AU - Roddan,A
AU - Xu,C
AU - Ajlouni,S
AU - Kakaletri,I
AU - Charalampaki,P
AU - Giannarou,S
DO - 10.1007/978-3-031-43895-0_54
EP - 585
PB - Springer Nature Switzerland
PY - 2023///
SN - 0302-9743
SP - 575
TI - Explainable image classification with improved trustworthiness for tissue characterisation
UR - http://dx.doi.org/10.1007/978-3-031-43895-0_54
UR - http://hdl.handle.net/10044/1/107744
ER -
The Hamlyn Centre
Bessemer Building
South Kensington Campus
Imperial College
London, SW7 2AZ
Map location
