A Machine Learning Approach to Cancer Detection and Localization Using Super Resolution Ultrasound Imaging

Georgios Papageorgiou, Mairead Butler, Andrew Mobberley, Weiping Lu, Julian Keanie, Daniel Good, Kevin Gallagher, Alan McNeill, Vassilis Sboros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


In this work, we present preliminary results on cancer detection and localization using super-resolution ultrasound imaging (SRUI) data. SRUI enables the visualization of the organ's physiology at micro-vascular level, which is of course related to the development of several major diseases, e.g., cancer. Our analysis serves as a feasibility study in our attempt to identify cancer regions using our SRUI algorithm on real prostate data. Despite the limited number of patients, we demonstrate that discrimination between healthy and tumorous regions using a popular anomaly detection technique, i.e., One-class Support Vector Machine, on SRUI data is indeed feasible. The problem is modeled as a binary classification task and relevant evaluation metrics are used for the evaluation of the method's performance.

Original languageEnglish
Title of host publicationIEEE International Ultrasonics Symposium 2022
ISBN (Electronic)9781665466578
Publication statusPublished - 1 Dec 2022
Event2022 IEEE International Ultrasonics Symposium - Venice, Italy
Duration: 10 Oct 202213 Oct 2022


Conference2022 IEEE International Ultrasonics Symposium
Abbreviated titleIUS 2022


  • anomaly detection
  • cancer localization
  • classification of medical images
  • micro-bubble localization and tracking
  • super-resolution ultrasound imaging

ASJC Scopus subject areas

  • Acoustics and Ultrasonics


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