M
Mishal N. Patel
Researcher at Royal Surrey County Hospital
Publications - 12
Citations - 258
Mishal N. Patel is an academic researcher from Royal Surrey County Hospital. The author has contributed to research in topics: Mammography & Medical imaging. The author has an hindex of 5, co-authored 12 publications receiving 225 citations. Previous affiliations of Mishal N. Patel include Institute of Cancer Research & University of Surrey.
Papers
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Journal ArticleDOI
Objective assessment of cancer genes for drug discovery
TL;DR: An objective, systematic, multifaceted computational assessment of biological and chemical space that can be applied to any human gene set to prioritize targets for therapeutic exploration is described.
Journal ArticleDOI
canSAR: an integrated cancer public translational research and drug discovery resource.
TL;DR: CanSAR can, in a single place, rapidly identify biological annotation of a target, its structural characterization, expression levels and protein interaction data, as well as suitable cell lines for experiments, potential tool compounds and similarity to known drug targets.
Proceedings ArticleDOI
The oncology medical image database (OMI-DB)
Mark D. Halling-Brown,Padraig T. Looney,Mishal N. Patel,Lucy M. Warren,Lucy M. Warren,Alistair Mackenzie,Alistair Mackenzie,Kenneth C. Young,Kenneth C. Young +8 more
TL;DR: A flexible oncology image repository, which prospectively collects images and data from multiple sites throughout the UK, and software and systems have been created to allow expert radiologists to annotate the images with interesting clinical features and provide descriptors of these features.
Proceedings ArticleDOI
A deep learning model observer for use in alterative forced choice virtual clinical trials
Majdi R. Alnowami,Majdi R. Alnowami,G. Mills,M. Awis,P. Elangovanr,Mishal N. Patel,Mark D. Halling-Brown,Kenneth C. Young,Kenneth C. Young,David R. Dance,David R. Dance,Kevin Wells +11 more
TL;DR: A Deep Learning Model Observer was developed and trained to identify lesion targets from normal tissue in small image segments, as used in Alternative Forced Choice (AFC) studies, and produced contrast thresholds equivalent to/better than human observer performance for spherical targets, and comparable for lesions targets.
Proceedings ArticleDOI
Automated collection of medical images for research from heterogeneous systems: trials and tribulations
TL;DR: This paper describes a semi-automated system, which comprehensively oversees the collection of both unprocessed and processed medical images from acquisition to a centralised database providing acentralised dataset for research.