Machine Learning, AI, Deep Learning

Machine Learning, AI, Deep Learning

I have worked in the areas of Machine Learning, AI, Deep Learning for some time. Some of my first publications back in 1999 and 2000 were on Neural Networks, which were rather simple (as shown in the architecture of the paper in Journal of Operations Research, 2000 shown below!) as compared with recent work.



Team members:

Collaborators:

Greg Slabaugh

Director of the Digital Environment Research Institute, Queen Mary, University of London

Jo Verhoeven

Division of Language and Communication Science, City, University of London

Karen Knapp

College of Medicine and Health, University of Exeter

Lucy Collinson

Electron Microscopy Science Technology Platform, Francis Crick Institute, UK

Martin Jones

Electron Microscopy Science Technology Platform, Francis Crick Institute, UK

Publications:

A multidisciplinary team and multiagency approach for AI implementation: A commentary for medical imaging and radiotherapy key stakeholders
Nikolaos Stogiannos, Caitlin Gillan, Helle Precht, Claudia sa dos Reis, Amrita Kumar, Tracy O'Regan, Vanessa Ellis, Anna Barnes, Richard Meades, Michael Pogose, Julien Greggio, Erica Scurr, Shamie Kumar, Graham King, David Rosewarne, Catherine Jones, Kicky G. van Leeuwen, Emma Hyde, Charlotte Beardmore, Juan Gutierrez Alliende, Samar El-Farra, Stamatia Papathanasiou, Jan Beger, Jonathan Nash, Peter van Ooijen, Christiane Zelenyanszki, Barbara Koch, Keith Antony Langmack, Richard Tucker, Vicky Goh, Tom Turmezei, Gerald Lip, Constantino Carlos Reyes-Aldasoro, Eduardo Alonso, Geraldine Dean, Shashivadan P. Hirani, Sofia Torre, Theophilus N. Akudjedu, Benard Ohene-Botwe, Ricardo Khine, Chris O'Sullivan, Yiannis Kyratsis, Mark McEntee, Peter Wheatstone, Yvonne Thackray, James Cairns, Derek Jerome, Andrew Scarsbrook, Christina Malamateniou
Published in Journal of Medical Imaging and Radiation Sciences, July 2024 (see publication)
Research themes: Image Processing, Machine Learning, AI, Deep Learning, Microscopy
Type: Journal
Predicting survival from colorectal cancer histology slides using deep learning A retrospective multicenter study
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A. Valous, Dyke Ferber, Lina Jansen, Constantino Carlos Reyes-Aldasoro, Inka Zörnig, Dirk Jäger, Hermann Brenner, Jenny Chang-Claude, Michael Hoffmeister, Niels Halama
Published in PLoS Medicine, January 2019 (see publication)
Research themes: Cancer, Machine Learning, AI, Deep Learning, Microscopy
Type: Paper