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Research

Biological systems - including the simplest cells - exhibit a broad range of functions to thrive in their environment. Research in the Imperial College Centre for Synthetic Biology is focused on the possibility of engineering the underlying biochemical processes to solve many of the challenges facing society, from healthcare to sustainable energy. In particular, we model, analyse, design and build biological and biochemical systems in living cells and/or in cell extracts, both exploring and enhancing the engineering potential of biology.

As part of our research we develop novel methods to accelerate the celebrated Design-Build-Test-Learn synthetic biology cycle. As such research in the Centre for Synthetic Biology highly multi- and interdisciplinary covering computational modelling and machine learning approaches; automated platform development and genetic circuit engineering ; multi-cellular and multi-organismal interactions, including gene drive and genome engineering; metabolic engineering; in vitro/cell-free synthetic biology; engineered phages and directed evolution; and biomimetics, biomaterials and biological engineering.

Publications

Citation

BibTex format

@inproceedings{Pan:2020:10.1007/978-3-030-59861-7_23,
author = {Pan, K and Hurault, G and Arulkumaran, K and Williams, H and Tanaka, R},
doi = {10.1007/978-3-030-59861-7_23},
pages = {220--230},
publisher = {Springer Verlag},
title = {EczemaNet: automating detection and severity assessment of atopic dermatitis},
url = {http://dx.doi.org/10.1007/978-3-030-59861-7_23},
year = {2020}
}

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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Atopic dermatitis (AD), also known as eczema, is one of themost common chronic skin diseases. AD severity is primarily evaluatedbased on visual inspections by clinicians, but is subjective and has largeinter- and intra-observer variability in many clinical study settings. Toaid the standardisation and automating the evaluation of AD severity,this paper introduces a CNN computer vision pipeline, EczemaNet, thatfirst detects areas of AD from photographs and then makes probabilisticpredictions  on  the  severity  of  the  disease.  EczemaNet  combines  trans-fer  and  multitask  learning,  ordinal  classification,  and  ensembling  overcrops to make its final predictions. We test EczemaNet using a set of im-ages acquired in a published clinical trial, and demonstrate low RMSEwith well-calibrated prediction intervals. We show the effectiveness of us-ing CNNs for non-neoplastic dermatological diseases with a medium-sizedataset, and their potential for more efficiently and objectively evaluatingAD severity, which has greater clinical relevance than mere classification.
AU  - Pan,K
AU  - Hurault,G
AU  - Arulkumaran,K
AU  - Williams,H
AU  - Tanaka,R
DO  - 10.1007/978-3-030-59861-7_23
EP  - 230
PB  - Springer Verlag
PY  - 2020///
SN  - 0302-9743
SP  - 220
TI  - EczemaNet: automating detection and severity assessment of atopic dermatitis
UR  - http://dx.doi.org/10.1007/978-3-030-59861-7_23
UR  - https://link.springer.com/chapter/10.1007/978-3-030-59861-7_23
UR  - http://hdl.handle.net/10044/1/82160
ER  -