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Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Bio
About me
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Posts
Pandemics, PhDs and pedagogy
Published:
An update
Getting started
Published:
This is my first blog post… ever! I have never really been one for blogging, but as I embark on a new journey I feel it will be useful to put my thoughts down and share them with colleagues, collaborators and students.
portfolio
Towards an open-source, equipment-agnostic framework for automated welfare monitoring
An NC3Rs-funded project on unsupervised anomaly detection of welfare events in videos of home-caged mice.
Weakly supervised brain tumour segmentation using generative adversarial networks
An EPSRC-funded project on weakly supervised brain tumour segmentation from multi-sequence MRI.
publications
High-throughput discovery of novel developmental phenotypes
Published in Nature, 2016
Using a standardized phenotyping platform that incorporates high-resolution 3D imaging, we identify phenotypes at multiple time points for previously uncharacterized genes and additional phenotypes for genes with previously reported mutant phenotypes.
Recommended citation: Dickinson, et al. (2016). "High-throughput discovery of novel developmental phenotypes." Nature. 537(7621). https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5295821/
Detection and characterisation of bone destruction in murine rheumatoid arthritis using statistical shape models
Published in Medical Image Analysis, 2017
This paper presents a fully automated method for obtaining quantitative measurements of bone destruction from volumetric micro-CT images of a mouse hind paw.
Recommended citation: Brown, et al. (2017). "Detection and characterisation of bone destruction in murine rheumatoid arthritis using statistical shape models" Medical Image Analysis. 40. https://www.sciencedirect.com/science/article/pii/S1361841517300774
Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks
Published in JAMA Ophthalmology, 2018
This fully automated algorithm diagnosed plus disease in ROP with comparable or better accuracy than human experts. This has potential applications in disease detection, monitoring, and prognosis in infants at risk of ROP.
Recommended citation: Brown, et al. (2018). "Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks" JAMA Ophthalmology. 136(7). https://jamanetwork.com/journals/jamaophthalmology/article-abstract/2680579
talks
Fully automated disease severity assessment and treatment monitoring in retinopathy of prematurity using deep learning
Published:
We show that our fully automated algorithm can be used to monitor the progression of plus disease over multiple patient visits with results that are consistent with the experts’ consensus diagnosis. Watch my talk here.
teaching
CMP2801M Advanced Programming
Undergraduate module, University of Lincoln, School of Computer Science, 2021
This module provides a comprehensive analysis of the general principles and practices of advancedprogramming with respect to software development. Notions and techniques of advancedprogramming are emphasised in the context of analysis, design and implementation of software andalgorithms. Great importance is placed upon the object-oriented paradigm and related conceptsapplied to algorithm and software development using the C++ programming language, howeverstudents will also be exposed to the principles and underlying theories pertaining to functionalprogramming.
CMP3108M Image Processing
Undergraduate module, University of Lincoln, School of Computer Science, 2021
This module aims to provide a broad introduction to the field of image processing, culminating in a practical understanding of how to apply and combine techniques to various image-related applications. The students will be able to extract useful information from the raw image and interpret the image data. The techniques will be implemented using the mathematical programming language Matlab or OpenCV.
CMP1124M Algorithms & Complexity
Undergraduate module, University of Lincoln, School of Computer Science, 2022
The module introduces the concepts of Algorithms and Complexity, providing an understanding of therange of applications where algorithmic solutions are required. Students will be introduced to theanalysis of time and space efficiency of algorithms; to the key issues in algorithm design; to the rangeof techniques used in the design of various types of algorithms. Students will be introduced torelevant theoretical concepts around algorithms and complexity in the lectures, together with apractical experience of implementing a range of algorithms in the workshops.