Synopsis
Artificial
intelligence (AI) and machine learning (ML) are advancing rapidly, as evidenced
by the recent success of AI systems in the automated diagnosis of cancerous
skin lesions from images or
autism from brain MRI. This talk will give course participants an
overview of current capability and future applications in the fields of image
interpretation, classification and analysis.
Highlights
·
Artificial intelligence and
machine learning methods are advancing rapidly.
·
These systems are now essential to add value to
the scan, and to reduce costs
·
Immediate applications can be found in
cases where there is a large amount of “ground truth” for training
Target Audience
Clinicians,
physicists and engineers seeking to learn more about the current state of the
art in computer aided diagnosis, and prepare for future innovations in this
area.Outcomes/Objectives
Artificial intelligence (AI) and machine learning
(ML) are advancing rapidly, as evidenced by the recent success of AI systems in
the automated diagnosis of cancerous skin lesions from images [1] or autism from brain MRI [2]. This
talk will give course participants an overview of current capability and future
applications in the fields of image interpretation, classification and analysis.Methods
AI encompasses a wide field, from image classification
[1], to playing poker [3]. Neural nets, support vector machines, random forests,
probabilistic boosting trees, and ridge regression methods have all been
applied to automatically generate features relevant to interpretation. The
advances in the last few years have been driven by improved processing power
and improved learning algorithms. These
methods are not limited to image interpretation. MR fingerprinting, for
example, can use ML methods to generate parametric maps from sparse sampling. Results
Deep
convolutional neural networks perform very well in classification and
identification tasks that have a lot of ground truth (annotated images). The
need for substantial amounts of training data limits some of these methods in
wider applications. However, several multinational companies now have access to very large data repositories. Alternatively, the business
model for several startup companies involves cloud processing of data, where
the system continually learns as more data is uploaded. Although this
technology has been applied as a post-processing tool, it can just as well be
applied to the image acquisition process. In many cases, using MRI to get a
human-interpretable image may not be the most cost effective use of a scanner. If
MRI is replaced by MR-AI, scanners may not need to make images at all, and the
cost of hardware may fall accordingly. Challenges now and in the future will involve
incorporation of these methods into the clinical workflow, and critical
examination of results. Conclusions
AI systems are poised to disrupt all aspects of ISMRM
activities, from image interpretation to scanner design. However: “Natural stupidity, rather than
artificial intelligence, remains the greatest risk” [4].Acknowledgements
No acknowledgement found.References
[1] A.
Esteva, B. Kuprel, R. A. Novoa, J. Ko, S. M. Swetter, H. M. Blau, et al., "Dermatologist-level
classification of skin cancer with deep neural networks," Nature, vol. 542, pp. 115-118, Feb 02
2017.
[2] H.
C. Hazlett, H. Gu, B. C. Munsell, S. H. Kim, M. Styner, J. J. Wolff, et al., "Early brain development
in infants at high risk for autism spectrum disorder," Nature, vol. 542, pp. 348-351, Feb 15
2017.
[3] M.
Moravcik, M. Schmid, N. Burch, V. Lisy, D. Morrill, N. Bard, et al., "DeepStack: Expert-level artificial intelligence in
heads-up no-limit poker," Science, Mar
02 2017.
[4] S.
Cave. (2017, 28 March). Intelligence: A
history. Available:
https://aeon.co/essays/on-the-dark-history-of-intelligence-as-domination