From Machine Learning to Accurate Cancer Diagnosis

Rafiee, R. (Advisor)

Activity: Talk or presentation typesPublic lecture/debate/seminar

Description

The classification of brain tumours is often achieved by tumour cells’ visual assessment using the microscope-based analysis of tumour samples on glass slides, termed histology. This type of assessment used for the diagnosis of a tumour sample can be very depending on the observer. Machine learning based approaches could improve cancer diagnosis without the need for such subjective diagnosis. In this talk, I would describe the development of MIMIC (minimal methylation classifier), a novel method for the routine detection of four molecular subgroups of medulloblastoma (the most common malignant childhood brain tumour) currently recognised by the World Health Organisation. I would also demonstrate its widespread potential in both immediate diagnostics application and research.
For more details: https://pure.qub.ac.uk/portal/files/137164452/s41598_017_13644_1.pdf
Now, clinically available: http://www.newgene.org.uk/medulloblastoma.htm
Period02 Oct 2019
Held atSchool of Electronics, Electrical Engineering and Computer Science
Degree of RecognitionLocal

Keywords

  • minimal methylation classifier
  • MIMIC
  • medulloblastoma
  • diagnosis
  • classification of brain tumours
  • World Health Organisation
  • Web-based software
  • improve cancer diagnosis without the need for such subjective diagnosis