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Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities
Context Aware Computing for The Internet of Things: A Survey
As we are moving towards the Internet of Things (IoT), the number of sensors
deployed around the world is growing at a rapid pace. Market research has shown
a significant growth of sensor deployments over the past decade and has
predicted a significant increment of the growth rate in the future. These
sensors continuously generate enormous amounts of data. However, in order to
add value to raw sensor data we need to understand it. Collection, modelling,
reasoning, and distribution of context in relation to sensor data plays
critical role in this challenge. Context-aware computing has proven to be
successful in understanding sensor data. In this paper, we survey context
awareness from an IoT perspective. We present the necessary background by
introducing the IoT paradigm and context-aware fundamentals at the beginning.
Then we provide an in-depth analysis of context life cycle. We evaluate a
subset of projects (50) which represent the majority of research and commercial
solutions proposed in the field of context-aware computing conducted over the
last decade (2001-2011) based on our own taxonomy. Finally, based on our
evaluation, we highlight the lessons to be learnt from the past and some
possible directions for future research. The survey addresses a broad range of
techniques, methods, models, functionalities, systems, applications, and
middleware solutions related to context awareness and IoT. Our goal is not only
to analyse, compare and consolidate past research work but also to appreciate
their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201
Theoretical and technological building blocks for an innovation accelerator
The scientific system that we use today was devised centuries ago and is
inadequate for our current ICT-based society: the peer review system encourages
conservatism, journal publications are monolithic and slow, data is often not
available to other scientists, and the independent validation of results is
limited. Building on the Innovation Accelerator paper by Helbing and Balietti
(2011) this paper takes the initial global vision and reviews the theoretical
and technological building blocks that can be used for implementing an
innovation (in first place: science) accelerator platform driven by
re-imagining the science system. The envisioned platform would rest on four
pillars: (i) Redesign the incentive scheme to reduce behavior such as
conservatism, herding and hyping; (ii) Advance scientific publications by
breaking up the monolithic paper unit and introducing other building blocks
such as data, tools, experiment workflows, resources; (iii) Use machine
readable semantics for publications, debate structures, provenance etc. in
order to include the computer as a partner in the scientific process, and (iv)
Build an online platform for collaboration, including a network of trust and
reputation among the different types of stakeholders in the scientific system:
scientists, educators, funding agencies, policy makers, students and industrial
innovators among others. Any such improvements to the scientific system must
support the entire scientific process (unlike current tools that chop up the
scientific process into disconnected pieces), must facilitate and encourage
collaboration and interdisciplinarity (again unlike current tools), must
facilitate the inclusion of intelligent computing in the scientific process,
must facilitate not only the core scientific process, but also accommodate
other stakeholders such science policy makers, industrial innovators, and the
general public
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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