Imperial College London


Faculty of MedicineDepartment of Surgery & Cancer




+44 (0)20 3313 3759eric.aboagye




Mrs Maureen Francis +44 (0)20 7594 2793




GN1Commonwealth BuildingHammersmith Campus






BibTex format

author = {Lavdas, I and Glocker, B and Rueckert, D and Taylor, SA and Aboagye, EO and Rockall, AG},
doi = {10.1016/j.crad.2019.01.012},
journal = {Clinical Radiology},
pages = {346--356},
title = {Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data},
url = {},
volume = {74},
year = {2019}

RIS format (EndNote, RefMan)

AB - Machine learning is now being increasingly employed in radiology to assist with tasks such as automatic lesion detection, segmentation, and characterisation. We are currently involved in an National Institute of Health Research (NIHR)-funded project, which aims to develop machine learning methods to improve the diagnostic performance and reduce the radiology reading time of whole-body magnetic resonance imaging (MRI) scans, in patients being staged for cancer (MALIBO study). We describe here the main challenges we have encountered during the course of this project. Data quality and uniformity are the two most important data traits to be considered in clinical trials incorporating machine learning. Robust data pre-processing and machine learning pipelines have been employed in MALIBO, a task facilitated by the now freely available machine learning libraries and toolboxes. Another important consideration for achieving the desired clinical outcome in MALIBO, was to effectively host the resulting machine learning output, along with the clinical images, for reading in a clinical environment. Finally, a range of legal, ethical, and clinical acceptance issues should be considered when attempting to incorporate computer-assisting tools into clinical practice. The road from translating computational methods into potentially useful clinical tools involves an analytical, stepwise adaptation approach, as well as engagement of a multidisciplinary team.
AU - Lavdas,I
AU - Glocker,B
AU - Rueckert,D
AU - Taylor,SA
AU - Aboagye,EO
AU - Rockall,AG
DO - 10.1016/j.crad.2019.01.012
EP - 356
PY - 2019///
SN - 0009-9260
SP - 346
TI - Machine learning in whole-body MRI: experiences and challenges from an applied study using multicentre data
T2 - Clinical Radiology
UR -
UR -
UR -
UR -
VL - 74
ER -