̽��ֱ�� of Cambridge - Carola-Bibiane Schönlieb

Trinity Challenge announces inaugural winners

Anonymous — Fri, 25 Jun 2021 14:43:12 +0000

̽��ֱ��eight winners have been selected by an international panel of expert judges, out of a total of 340 applications from 61 countries. ̽��ֱ��competition has seen unprecedented collaborations between the private, public, charitable and academic sectors, and will drive a step-change in using data and analytics for pandemic preparedness.

̽��ֱ�� ̽��ֱ�� of Cambridge joined a coalition of some of the world’s leading businesses and academic and tech institutions to launch ̽��ֱ��Trinity Challenge in September 2020. ̽��ֱ��global challenge, convened by Dame Sally Davies, Master of Trinity College, provides a £10m prize fund for breakthrough solutions to make sure one billion more people are better protected against health emergencies.

Participatory One Health Disease Detection (PODD), which empowers farmers to identify and report zoonotic diseases that could potentially pass from animals to humans, has been named the grand prize winner at the inaugural awards ceremony. ̽��ֱ��organisation is being awarded £1.3 million (US$1.8 million) in pledged funding.

Led by Susumpat Patipow, General Director at OpenDream, PODD has developed a platform for livestock owners to report suspected animal illness, and in return receive veterinary care to improve animal health. If it appears a disease outbreak is likely, local health officials will quarantine the sick animals, saving the remaining livestock and possibly preventing the next COVID-19-type outbreak.

Having already achieved significant success in Thailand, with a network of 20,000 farmers helping to detect and control disease outbreaks, PODD is looking to expand its operations to Cambodia, India, Indonesia, Laos, Uganda and Vietnam over the next three years.

BloodCounts! - an international consortium of scientists, led by Professor Carola-Bibiane Schönlieb from Cambridge’s Department of Applied Mathematics and Theoretical Physics (DAMTP) that has developed an innovative infectious disease outbreak detection system, was one of two second-prize winners, each awarded £1 million in pledged funding.

Developed by Dr Michael Roberts and Dr Nicholas Gleadall, the BloodCounts! Solution uses data from routine blood tests and powerful AI-based techniques to provide a ‘tsunami-like’ early warning system for new disease outbreaks.

“Since the beginning of the pandemic I have been developing AI-based methods to aid in medical decision making for COVID-19 patients, starting with analysis of Chest X-ray data,” said Roberts, who is affiliated with DAMTP and the Cambridge Mathematics of Information in Healthcare (CMIH) Hub. “Echoing the observations made by the clinical teams, we saw profound and unique differences in the medical measurements of infected individuals, particularly in their full blood count data. It is these changes that we can train models to detect at scale.”

Unlike many current test methods, their approach doesn't require any prior knowledge of a specific pathogen to work, instead, they use full blood count data to exploit the pathogen detecting abilities of the human immune system by observing changes in the blood measurements associated with infection.

As the full blood count is the world’s most common medical laboratory test, with over 3.6 billion being performed worldwide each year, the BloodCounts! team can rapidly apply their methods to scan for abnormal changes in the blood cells of large populations - alerting public health agencies to potential outbreaks of pathogen infection.

This solution is a demonstration of how the application of AI-based methods can lead to healthcare benefits. It also highlights the importance of strong collaboration between leading organisations, as the development of these algorithms was only possible due the EpiCov data sharing initiative pioneered by Cambridge ̽��ֱ�� Hospitals.

“Hundreds of millions of full blood count tests are being performed every day worldwide, and this meant that we could apply our AI methods at population scale,” said Gleadall, from the ̽��ֱ�� of Cambridge and NHS Blood and Transplant. “Usually the rich measurement data are discarded after summary results have been reported, but by working with Cambridge ̽��ֱ��, Barts Health London, and ̽��ֱ�� College London NHS Hospitals we have rescued throughout the pandemic the rich data from 2.8 million full blood count tests.”

̽��ֱ��Sentinel Forecasting System is the other second-prize winner, and will explore the emergence of new infectious diseases in West Africa, beginning with Lassa fever. ̽��ֱ��system will combine data from ecology, social science, genomics and epidemiology to provide real-time disease risk for haemorrhagic fevers, such as Lassa and Ebola.

Lassa is a virus usually passed to humans through exposure to food or household items contaminated by infected rats. It is endemic in West African countries including Benin, Ghana, Guinea, Liberia, Mali, Sierra Leone, Togo and Nigeria.

Around 80% of people who become infected with Lassa virus have no symptoms, and the overall case-fatality rate is 1%. 1 in 5 infections can result in severe disease affecting the liver, spleen and kidneys.

̽��ֱ��UCL team will partner with the African Centre of Excellence for Genomics of Infectious Diseases in Nigeria, Nigeria Centre for Disease Control, Zoological Society of London, London School of Hygiene and Tropical Medicine, Microsoft, and Cambridge’s Laboratory of Viral Zoonotics (LVZ) to produce the system.

“This Trinity Challenge project brings new multidisciplinary technologies together to anticipate climatic, human, animal population, agricultural impacts on the likelihood of spill overs of infections from animals to humans,” said Professor Jonathan Heeney, who leads LVZ at Cambridge’s Department of Veterinary Medicine.

Additionally, five 3rd prize winners are each being awarded £480,000 (US$ 660,000) in pledged funding.

Dame Sally Davies said: “It was crystal clear at the beginning of this pandemic that the world had a lack of data, a lack of access to data, and a lack of interoperability of data, presenting a challenge. While others talked, we took action. ̽��ֱ��solutions we have discovered in the course of the Challenge will be a link between systems and countries.”

In addition to financial support, ̽��ֱ��Trinity Challenge will provide connections to the right organisations to maximise the impact of these solutions. Since its inception nine months ago, TTC has united early applicants with partners from the private, academic and social sectors to receive access to digital platforms, data, and technical advice, to scale-up the use of data and analytics to protect the world from future health emergencies. ̽��ֱ��Trinity Challenge has helped form over 200 connections between applicants and its members.

̽��ֱ��Trinity Challenge has announced the winners of its inaugural competition, and is investing a £5.7 million (US$8 million) charitable pledged prize fund into one grand prize winner, two 2nd prize winners, and five 3rd prize winners.

While others talked, we took action. ̽��ֱ��solutions we have discovered in the course of the Challenge will be a link between systems and countries

Dame Sally Davies

̽��ֱ��Trinity Challenge

Collage of Trinity Challenge finalists

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Yes

Cambridge helps launch £10m Trinity Challenge to protect the world against future pandemics

cjb250 — Mon, 14 Sep 2020 07:00:11 +0000

̽��ֱ��Trinity Challenge sets a series of urgent questions to harness the potential of data and analytics to learn and share lessons from the great innovations made to combat COVID-19 and to build resilience against future health emergencies. £10m of funding will be made available to teams to support and scale their innovations across areas including economics, behavioural sciences, and epidemiology.

̽��ֱ��Challenge has been convened by Dame Sally Davies, Master of Trinity College, Cambridge. Its Founding Members represent a diverse coalition of world leading organisations across the private, public and social sectors. All are united by the common aim of using data and advanced analytics to develop insights and practical actions to contribute to a world better protected from health emergencies.

Launching the Challenge, Dame Sally said: “There will be another COVID-19, and there is an opportunity for the international community to learn lessons now and prepare for the future. ̽��ֱ��Trinity Challenge is a recognition by business, academia and philanthropy of the need for new, breakthrough ideas and approaches to beat the next pandemic.”

̽��ֱ��Challenge has the support of Professor Stephen J Toope, Vice-Chancellor of the ̽��ֱ�� of Cambridge, who said: “COVID-19 has highlighted the power of data in helping us understand and tackle health emergencies, but it has also revealed the challenges we face in getting the right data to the right people at the right time.

“We need to up our collaborative game so that we cross sectors, disciplines and borders to help us tackle – and even prevent – future health emergencies. ̽��ֱ��Trinity Challenge is an exciting opportunity to make this happen.”

Professor Carola-Bibiane Schönlieb and Anna Vignoles, co-chairs of the Cambridge Centre for Data-Driven Discovery, will be providing leadership on behalf of the ̽��ֱ��.

Professor Schönlieb said: “Cambridge is supporting ̽��ֱ��Trinity Challenge not only because of our expertise and ability to contribute to tackling this pandemic but also because of our key role in producing the next generation of scientists who will be needed to tackle this and future public health threats of this kind.”

Professor Vignoles added: “Our ̽��ֱ�� is world leading in many aspects of the science needed to tackle this pandemic. We are also world leading in terms of our data science. ̽��ֱ��Cambridge Centre for Data-Driven Discovery convenes data science expertise from right across Cambridge and is highly interdisciplinary in its focus. We have been working closely with ̽��ֱ��Trinity Challenge team to determine what data we need and how we might use it to address this pandemic.”

̽��ֱ��Trinity Challenge is calling on global participants to submit impact-led ideas on how to safeguard our health and economic systems from the threat of global health emergencies. Submissions that make the selection will be supported with access to people, data and resources from Founding Members, to maximise the effectiveness of their solutions and leverage the world-leading expertise and innovation of these institutions.

Challenge Teams will focus on potential solutions that support and strengthen the global public health ecosystem in a robust and inclusive way. Solutions will be fielded globally through an open and accessible submission process to bring the best minds and ideas together with the aim of developing insights that will benefit the world in the future.

̽��ֱ��Founding Members are: Aviva, Bill and Melinda Gates Foundation, Brunswick Group, ̽��ֱ�� of Cambridge, Discovery Limited, Facebook, Global Virome Project, Google, HKUMed, Imperial College London, Institute for Health Metrics and Evaluation, Internews, Legal and General, LSE, McKinsey and Company, Microsoft, Northeastern ̽��ֱ��, Optum, Reckitt Benckiser, Tencent, Zenysis Technology.

Full details are available on ̽��ֱ��Trinity Challenge website.

̽��ֱ�� ̽��ֱ�� of Cambridge has joined a coalition of some of the world’s leading businesses and academic and tech institutions to launch ̽��ֱ��Trinity Challenge. This global challenge provides a £10m prize fund for breakthrough solutions to make sure one billion more people are better protected against health emergencies.

COVID-19 has highlighted the power of data in helping us understand and tackle health emergencies, but it has also revealed the challenges we face in getting the right data to the right people at the right time

Stephen J Toope

Trinity Challenge

Dame Sally Davies and ̽��ֱ��Trinity Challenge

Yes

Harnessing AI in the fight against COVID-19

sc604 — Thu, 04 Jun 2020 01:00:00 +0000

An open-source artificial intelligence (AI) tool, combining chest imaging data with laboratory and clinical data, is being developed by Cambridge researchers to support the rapid diagnosis and triaging of patients with COVID-19 in the UK.

̽��ֱ��team, led by Professors Carola-Bibiane Schönlieb and Evis Sala, brings together expertise in AI for imaging with expertise in radiology and clinical applications from Addenbrooke’s and Papworth Hospitals, as well as collaborators from the UK, China, Austria and Italy, to develop a prediction model that can rapidly and reliably diagnose and suggest a prognosis to doctors.

Reverse-transcription polymerase chain reaction (RT-PCR) tests are currently the most common tool used to diagnose COVID-19, but they are only up to 70% sensitive, meaning there are up to 30% false negatives.

While chest X-rays and CT scans provide valuable diagnostic and monitoring information that can complement laboratory and clinical data, it is a complex task typically done by radiologists, whose expertise is often in high demand. Fast and accurate diagnosis of patients in order to limit disease spread, together with the rapid determination of whether a patient is likely to recover, require intensive care unit (ICU) admission, or intensive ventilation, is key to allocating resources and to improving patient outcomes.

“AI offers huge potential to support agile clinical decision making, ensuring patients receive the most appropriate support and leading to better patient outcomes,” said Sala, who is based at the Department of Radiology.

Recent studies have suggested that using AI could have a meaningful impact on the management of patients with COVID-19. AI tools such as deep learning can offer automated image analysis and integration with clinical data to help clinicians make more informed decisions for treatment.

However, good quality data and computing power are required to train and optimise predictive AI models and data availability is a major bottleneck when developing new systems. Coupled with this, the lack of standardisation of datasets makes it challenging to reuse existing AI tools in a different country than the one that it was trained for. Most current AI tools have been developed on small, locally collected datasets. Data that is being collected in hospitals all over the world varies in what is being collected and how the data is processed. Therefore, an effort for developing a widely applicable tool for COVID-19 hospital support must be open source so it can be adapted to different environments; be based on a serious data sharing and data curation, data cleaning and standardisation effort; and be developed with mathematical, statistical and engineering expertise to develop robust and translatable tools.

To address these challenges, the team from the Cambridge Centre for Mathematical Imaging in Healthcare (CMIH) is developing a flexible, open-source AI tool that could be used by hospitals worldwide. Drawing on their history of global research collaboration and expertise in data governance, the team is gathering datasets from Austria, China, Italy and the UK for their work. Data scientists and clinicians are working in close collaboration, following standard protocols to identify bias during development. “Rigorous mathematical models play a key role in mitigating bias and improving the efficacy of the prediction model as they follow universal rules with mathematical guarantees,” said Schönlieb.

Using deep learning approaches along with mathematical and statistical analysis methods, the new tool will be accompanied by a comprehensive algorithmic strategy that will allow fine-tuning for datasets with different characteristics and implementation in different countries. ̽��ֱ��team are hoping to launch the AIX-COV-NET tool within the next 12 to 18 months. ̽��ֱ��project has recently received funding from the EU-funded Innovative Medicines Initiative and Intel.

“Our team’s strength is the close dialogue we have between clinicians and data scientists, and the passion we all bring for advancing AI solutions for COVID-19,” said Schönlieb, from Cambridge's Department of Applied Mathematics and Theoretical Physics.

“AI offers huge potential to support agile clinical decision making, ensuring patients receive the most appropriate support and leading to better patient outcomes,” said Sala.

̽��ֱ��core project team is comprised of data scientists and clinicians from across Cambridge and is led by Professor Carola-Bibiane Schönlieb, Director of the Centre for Mathematical Imaging in Healthcare, and Professor Evis Sala, Professor of Oncological Imaging, ̽��ֱ�� of Cambridge & Honorary Consultant Radiologist, Addenbrooke’s Hospital, Cambridge ̽��ֱ�� Hospitals NHS Foundation Trust. ̽��ֱ��team is supported by an AI and image analysis team, drawn from subject experts across Cambridge and around the world, a clinical team comprised of colleagues from hospitals in Cambridge, London and Vienna, and a support team based in the Faculty of Mathematics. Partner institutions include hospitals in Wuhan, China; Milan, Italy; and Madrid, Spain, and universities in Manchester, Vienna and London.

̽��ֱ�� ̽��ֱ�� of Cambridge has an impressive record of achievement in multidisciplinary research and innovation. ̽��ֱ��CMIH is a collaboration between mathematics, engineering, physics and biomedical scientists and clinicians and is one of five centres to receive investment from the Engineering and Physical Sciences Research Council (EPSRC). A key aim of this partnership is the delivery of high quality, multidisciplinary research that will help overcome some of the major challenges facing the NHS.

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AI assisted COVID-19 diagnostic and prognostic tool could improve resource allocation and patient outcomes.

AI offers huge potential to support agile clinical decision making, ensuring patients receive the most appropriate support and leading to better patient outcomes

Evis Sala

Yes

Global collaboration takes off

ag236 — Mon, 12 Dec 2016 14:35:01 +0000

An alliance between the ̽��ֱ�� of California Berkeley, the ̽��ֱ�� of Cambridge and the National ̽��ֱ�� of Singapore has moved into its next phase following the first joint call for research proposals and the approval of five inaugural projects.

̽��ֱ��successful proposals, in the areas of “Cities”, “Precision medicine” and “Smart systems”, will be supported through a joint fund of £723,900 –including a contribution of £301,000 from the ̽��ֱ�� of Cambridge.

These are the first projects set up under the auspices of the Global Alliance, a partnership between UC Berkeley, Cambridge and NUS formalised at the end of 2015.

̽��ֱ��aim of the partnership is to promote collaborative and multidisciplinary research on a global scale.

Its focus is on finding solutions to global challenges that cannot be solved by a single institution, or even through bilateral collaboration.

Prof Chris Abell, Pro-Vice-Chancellor for Research at the ̽��ֱ�� of Cambridge, said:

“ ̽��ֱ��projects supported under this first call show the many ways in which our joint resources will let us tackle global problems more effectively. They are the first in a series of research collaborations that will allow our three institutions to work together for the global good.”

̽��ֱ��five successful projects were:

“Toward an open and secure internet-of-things reference platform” (Cambridge PI: Prof Simon Moore)
“Modelling interacting high-dimensional phenotypes – Kronecker Products for imaging, genetics and imaging genetics” (Cambridge PI: Prof John Aston)
“Mathematical and statistical theory of imaging” (Cambridge PI: Dr Carola-Bibiane Schönlieb)
“Smart design: human-centric planning of urban districts” (Cambridge PI: Prof Koen Steemers)
“Translucent city” (Cambridge PI: Dr. Ruchi Choudhary)

Funding for the five projects will be released between November 2016 and February 2017.

A further call for proposals will be published in May 2017 (deadline September 2017).

For more information, contact Dr Kata Fülöp, International Strategy Office, Kata.Fulop@admin.cam.ac.uk

Funding approved for research projects involving UC Berkeley, Cambridge and the National ̽��ֱ�� of Singapore

̽��ֱ��projects supported under this first call show the many ways in which our joint resources will let us tackle global problems more effectively.

Prof Chris Abell

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Time travelling to the mother tongue

lw355 — Tue, 19 Jul 2016 08:00:51 +0000

No matter whether you speak English or Urdu, Waloon or Waziri, Portuguese or Persian, the roots of your language are the same. Proto-Indo-European (PIE) is the mother tongue – shared by several hundred contemporary languages, as well as many now extinct, and spoken by people who lived from about 6,000 to 3,500 BC on the steppes to the north of the Caspian Sea.

They left no written texts and although historical linguists have, since the 19th century, painstakingly reconstructed the language from daughter languages, the question of how it actually sounded was assumed to be permanently out of reach.

Now, researchers at the Universities of Cambridge and Oxford have developed a sound-based method to move back through the family tree of languages that stem from PIE. They can simulate how certain words would have sounded when they were spoken 8,000 years ago.

Remarkably, at the heart of the technology is the statistics of shape.

“Sounds have shape,” explains Professor John Aston, from Cambridge’s Statistical Laboratory. “As a word is uttered it vibrates air, and the shape of this soundwave can be measured and turned into a series of numbers. Once we have these stats, and the stats of another spoken word, we can start asking how similar they are and what it would take to shift from one to another.”

A word said in a certain language will have a different shape to the same word in another language, or an earlier language. ̽��ֱ��researchers can shift from one shape to another through a series of small changes in the statistics. “It’s more than an averaging process, it’s a continuum from one sound to the other,” adds Aston, who is funded by the Engineering and Physical Sciences Research Council (EPSRC). “At each stage, we can turn the shape back into sound to hear how the word has changed.”

Rather than reconstructing written forms of ancient words, the researchers triangulate backwards from contemporary and archival audio recordings to regenerate audible spoken forms from earlier points in the evolutionary tree. Using a relatively new field of shape-based mathematics, the researchers take the soundwave and visualise it as a spectrogram – basically an undulating three-dimensional surface that represents the shape of that sound – and then reshape the spectrogram along a trajectory ‘signposted’ by known sounds.

While Aston leads the team of statistician ‘shape-shifters’ in Cambridge, the acoustic-phonetic and linguistic expertise is provided by Professor John Coleman’s group in Oxford.

̽��ֱ��researchers are working on the words for numbers as these have the same meaning in any language. ̽��ֱ��longest path of development simulated so far goes backwards 8,000 years from English one to its PIE ancestor oinos, and likewise for other numerals. They have also ‘gone forwards’ from the PIE penkwe to the modern Greek pente, modern Welsh pimp and modern Englishfive, as well as simulating change from Modern English to Anglo-Saxon (or vice versa), and from Modern Romance languages back to Latin.

(Other audio demonstrations are available here)

“We’ve explicitly focused on reproducing sound changes and etymologies that the established analyses already suggest, rather than seeking to overturn them,” says Coleman, whose research was funded by the Arts and Humanities Research Council.

They have discovered words that appear to correctly ‘fall out’ of the continuum. “It’s pleasing, not because it overturns the received wisdom, but because it encourages us that we are getting something right, some of the time at least. And along the way there have also been a few surprises!” ̽��ֱ��method sometimes follows paths that do not seem to be etymologically correct, demonstrating that the method is scientifically testable and pointing to areas in which refinements are needed.

Remarkably, because the statistics describe the sound of an individual saying the word, the researchers are able to keep the characteristics of pitch and delivery the same. They can effectively turn the word spoken by someone in one language into what it would sound like if they were speaking fluently in another.

They can also extrapolate into the future, although with caveats, as Coleman describes: “If you just extrapolate linearly, you’ll reach a point at which the sound change hits the limit of what is a humanly reasonable sound. This has happened in some languages in the past with certain vowel sounds. But if you asked me what English will sound like in 300 years, my educated guess is that it will be hardly any different from today!”

For the team, the excitement of the research includes unearthing some gems of archival recordings of various languages that had been given up for dead, including an Old Prussian word last spoken by people in the early 1700s but ‘borrowed’ into Low Prussian and discovered in a German audio archive.

Their work has applications in automatic translation and film dubbing, as well as medical imaging (see panel), but the principal aim is for the technology to be used alongside traditional methods used by historical linguists to understand the process of language change over thousands of years.

“From my point of view, it’s amazing that we can turn exciting yet highly abstract statistical theory into something that really helps explain the roots of modern language,” says Aston.

“Now that we’ve developed many of the necessary technical methods for realising the extraordinary ambition of hearing ancient sounds once more,” adds Coleman, “these early successes are opening up a wide range of new questions, one of the central being how far back in time can we really go?”

Audio demonstrations are available here: www.phon.ox.ac.uk/jcoleman/ancient-sounds-audio.html

Inset image: Spectrograms showing how the shape of the sound of a word in one language can be morphed into the sound of the same word in another language; credit: John Aston.

̽��ֱ��sounds of languages that died thousands of years ago have been brought to life again through technology that uses statistics in a revolutionary new way.

As a word is uttered it vibrates air, and the shape of this soundwave can be measured and turned into a series of numbers

John Aston

Spectrogram showing the shape of the sound of a word

Medical imaging reshaped

̽��ֱ��statistics of shape are not just being used to show how different languages relate to each – they are also being used to improve the analysis of medical images.

Just as soundwaves have a shape that can be analysed using statistics, so do the patterns of neurons interacting with each other or the dimensions of the surface of a tumour. Now a new research Centre will develop tools that use the mathematics of the shapes found in medical images to improve diagnosis, prognosis and treatment planning for patients.

̽��ֱ��EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, one of five ‘maths’ centres recently funded by £10 million from EPSRC, is co-led by Aston and Dr Carola-Bibiane Schönlieb from the Department of Applied Mathematics and Theoretical Physics in Cambridge.

“ ̽��ֱ��new methodologies will allow clinical medicine to move beyond one person reading single scans, to automated systems capable of analysing populations of images,” explains Schönlieb. “As a result, clinicians will have far greater scope to ask complex questions of the medical image.”

It’s already possible to extract statistical information from an image of a patient’s thigh bone, turn the data into a template for comparison with those from other people in the population, and then ask whether a particular shape of bone is more prone to being broken than others in the elderly.

Most organ scans split the image into many elements, which are then analysed voxel by voxel. “But complex structures like the heart and the brain should be analysed holistically,” explains Dr James Rudd, from the Department of Medicine, who leads the clinical interaction with the Centre. “ ̽��ֱ��tools we are developing will enable the analysis of organs like the brain as single objects with millions of connections.”

̽��ֱ��Centre brings together researchers and clinicians from applied and pure maths, engineering, physics, biology, oncology, clinical neuroscience and cardiology, and involves industrial partners Siemens, AstraZeneca, Microsoft, GSK and Cambridge Computed Imaging.

̽��ֱ��text in this work is licensed under a Creative Commons Attribution 4.0 International License. For image use please see separate credits above.

Yes

Imaging: interpreting the seen and discovering the unseen

lw355 — Mon, 02 Feb 2015 09:59:56 +0000

We humans are visual creatures. An image aims to depict reality to us, but also invokes our imagination. It speaks more than a thousand words. We live in a world saturated with images and images allow us to see this world – from brain cells to distant galaxies – as never before. Advanced imaging techniques enable us to ask new research questions, break down disciplinary boundaries and extend our knowledge across an immense variety of fields.

Scientists have always used images of various kinds – drawings, pictures, photographs and videos, to name a few – to make discoveries, describe processes in nature, catalogue and achieve specimens, and illustrate observations and ideas. In scientific discoveries, images are often the scientific finding itself.

A work of art, an image in itself, can be analysed and its making can be understood with the help of advanced scientific imaging techniques. New images are created by this analysis, and the worlds of arts and science are becoming increasingly overlapping.

Scientific imaging has never been as exciting as it is now, with new technologies emerging all the time. ̽��ֱ��resolution limit in light microscopy, which had seemed unbreakable, is now less than 100 nanometres. These advances in super-resolved fluorescence microscopy were recognised in 2014 by the awarding of the Nobel Prize in Chemistry to Eric Betzig and W. E. Moerner in the USA and Stefan Hell in Germany.

Cambridge is home to a wealth of research which includes developing tools for acquisition, visualisation, automated processing and analysis of images. In January 2014, a group was formed to connect, present, discuss and advance research on or with images. IMAGES brings together leading academics from across the disciplines, as well as international experts and research-led industries that work on pioneering imaging technologies and analytical algorithms.

̽��ֱ��complex process, from acquiring images, to their interpretation and problem-solving applications, requires multi-expertise partnerships. Different problems and image applications inform similar methodologies and interpretative strategies. Cross-disciplinary collaboration is needed to analyse the image information not explicit in machine-generated data.

Mathematicians, physicists, chemists and biologists work together to develop new instruments, chemical dyes and model systems to interrogate biological questions with more precision and at greater resolution.

At CRUK Cambridge Institute and the Department of Applied Mathematics and Theoretical Physics, microscopists and mathematicians are developing new ways of tracking cells and analysing the effect of cancer drugs in tissues and whole organisms.

At the Cambridge Biomedical Campus, clinicians use magnetic resonance, positron emission tomography, and acoustic imaging as tools for looking into our internal organs. ̽��ֱ��challenge here is to produce a high quality description of patients and their ailments from data that is necessarily limited by the capability of scanners and the need to minimise exposure to harmful radiation.

Mathematicians and engineers create automated image-processing and analysis algorithms that extract meaningful, essential information from often large-scale, high-dimensional and imperfect image data.

̽��ֱ��importance of reliable image analysis extends to astronomy, the arts, seismology, surveillance and security. Image de-noising and image restoration algorithms are also essential pieces of any further image analysis pipeline such as object segmentation and tracking, pattern recognition, in fact any quantitative and qualitative analysis of image content.

At the Fitzwilliam Museum and the Hamilton Kerr Institute, spectroscopy methods underpin the non-invasive analyses of artists’ materials and techniques, informing the conservation and cross-disciplinary interpretation of paintings, illuminated manuscripts and Egyptian papyri. ̽��ֱ��research unites imaging scientists, chemists, physicists, mathematicians, biologists, conservators, artists and historians. Thanks to cutting-edge imaging techniques, we can now see art works as never before, uncovering centuries-long secrets of their production and ensuring their preservation into the future.

̽��ֱ��IMAGES group aims to stimulate new inquiries and focused dialogues between these many disciplines across the sciences, arts and humanities by providing them with a platform for communication. As collaborations across the ̽��ֱ�� show, art and science are not disparate, but complementary ways of seeing the world. Both depend on the subtle observations of life and attempt to interpret the seen and discover the unseen.

Dr Stella Panayotova (Fitzwilliam Museum Cambridge), Dr Stefanie Reichelt (Cancer Research UK Cambridge Institute) and Dr Carola-Bibiane Schönlieb (Department of Applied Mathematics and Theoretical Physics) lead IMAGES

From visualising microscopic cells to massive galaxies, imaging is a core tool for many disciplines, and it’s also the basis of a surge in recent technical developments – some of which are being pioneered in Cambridge. Today, we begin a month-long focus on research that is exploring far beyond what the eye can see, introduced here by Stella Panayotova, Stefanie Reichelt and Carola-Bibiane Schönlieb.

IMAGES

̽��ֱ��text in this work is licensed under a Creative Commons Licence. If you use this content on your site please link back to this page. For image rights, please see the credits associated with each individual image.

Yes