Automated deep learning-based assessment of tumour-infiltrating lymphocyte density determines prognosis in colorectal cancer.

Abstract

Authors

• Joshua Millward
  School of Computing, Engineering and Mathematical Sciences, La Trobe University, Melbourne, Australia. j.millward@latrobe.edu.au.
• Zhen He
• Aiden Nibali
  Department of Computer Science and Computer Engineering, La Trobe University, Melbourne, Australia. anibali@students.latrobe.edu.au.
• Dmitri Mouradov
  Personalised Oncology Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.
• Lisa A Mielke
  Olivia Newton-John Cancer Research Institute, Melbourne, Australia.
• Kelly Tran
  Olivia Newton-John Cancer Research Institute, Melbourne, Australia.
• Angela Chou
  Royal North Shore Hospital and Northern Clinical School, Sydney Medical School, University of Sydney, Sydney, Australia.
• Nicholas J Hawkins
  School of Biomedical Sciences, UNSW Sydney, Sydney, Australia.
• Robyn L Ward
  School of Medical Sciences, Faculty of Medicine and Health, University of New South Wales Sydney, Kensington, NSW, Australia; Faculty of Medicine and Health, The University of Sydney, Camperdown, NSW, Australia.
• Anthony J Gill
  Royal North Shore Hospital, Cancer Diagnosis and Pathology Group, Kolling Institute of Medical Research, Sydney, Australia.
• Oliver M Sieber
  Personalised Oncology Division, The Walter and Eliza Hall Institute of Medial Research, 1G Royal Parade, Parkville, Melbourne, VIC, 3052, Australia.
• David S Williams
  Department of Ophthalmology and Stein Eye Institute, University of California, Los Angeles, CA 90095, USA; Department of Neurobiology, David Geffen School of Medicine at UCLA, University of California, Los Angeles, CA 90095, USA; Molecular Biology Institute, University of California, Los Angeles, CA 90095, USA; Brain Research Institute, University of California, Los Angeles, CA 90095, USA. Electronic address: dswilliams@ucla.edu.

Keywords

External Resources