Keynote: Beyond Supervised Learning Exploring Novel Machine Learning Approaches for Robust Medical Image Analysis
Kainz B (2023)
Publication Type: Conference contribution
Publication year: 2023
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Pages Range: 1-
Conference Proceedings Title: Informatik aktuell
Event location: Braunschweig
ISBN: 9783658416560
DOI: 10.1007/978-3-658-41657-7_1
Abstract
Machine learning has been widely regarded as a solution for diagnostic automation in medical image analysis, but there are still unsolved problems in robust modelling of normal appearance and identification of features pointing into the long tail of population data. In this talk, I will explore the fitness of machine learning for applications at the front line of care and high throughput population health screening, specifically in prenatal health screening with ultrasound and MRI, cardiac imaging, and bedside diagnosis of deep vein thrombosis. I will discuss the requirements for such applications and how quality control can be achieved through robust estimation of algorithmic uncertainties and automatic robust modelling of expected anatomical structures. I will also explore the potential for improving models through active learning and the accuracy of nonexpert labelling workforces. However, I will argue that supervised machine learning might not be fit for purpose, as it cannot handle the unknown and requires a lot of annotated examples from well-defined pathological appearance. This categorization paradigm cannot be deployed earlier in the diagnostic pathway or for health screening, where a growing number of potentially hundred-thousands of medically catalogued illnesses may be relevant for diagnosis. Therefore, I introduce the idea of normative representation learning as a new machine learning paradigm for medical imaging. This paradigm can provide patient-specific computational tools for robust confirmation of normality, image quality control, health screening, and prevention of disease before onset. I will present novel deep learning approaches that can learn without manual labels from healthy patient data only. Our initial success with single class learning and self-supervised learning will be discussed, along with an outlook into the future with causal machine learning methods and the potential of advanced generative models [1].
Authors with CRIS profile
How to cite
APA:
Kainz, B. (2023). Keynote: Beyond Supervised Learning Exploring Novel Machine Learning Approaches for Robust Medical Image Analysis. In Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff (Eds.), Informatik aktuell (pp. 1-). Braunschweig, DE: Springer Science and Business Media Deutschland GmbH.
MLA:
Kainz, Bernhard. "Keynote: Beyond Supervised Learning Exploring Novel Machine Learning Approaches for Robust Medical Image Analysis." Proceedings of the Bildverarbeitung für die Medizin Workshop, BVM 2023, Braunschweig Ed. Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus Maier-Hein, Christoph Palm, Thomas Tolxdorff, Springer Science and Business Media Deutschland GmbH, 2023. 1-.
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