Machine Learning in Medical Physics workshop
The application of Artificial Intelligence (AI) is on the rise in many industries, especially for the use of automating image analysis, an implementation that would highly benefit the medical field.
QUASAR Group member and LIV.DAT student Selina Dhinsey attended an IPEM workshop on Introductions to Machine Learning within Medical Physics in Birmingham, UK. The workshop consisted of a series of talks ranging from the basic concepts of machine learning to more guided examples of applying machine learning and the challenges facing it.
As an IPEM event, the audience was very varied and made for insightful discussions during the networking session between clinicians, pathologists, radiographers and students, all interested in how machine learning could aid their respective fields.
One of the invited speakers, Dr Zach Eaton-Rose kindly presented the capabilities of niftynet, a convolutional neural network platform for medical image analysis and image-guided therapy. It incorporates easy to customise features and pre-trained models to allow even a beginner to apply machine learning to their data.
Selina hopes to use the ideas and methods discussed during the workshop as a base to implement in her own work of automating the analysis of comet assay images for determining the level of damage caused to cellular DNA following irradiation.