Director Robert Jenssen gave a talk and took part in a panel debate at the Norwegian Hospital Pharmacists Association’s conference on how AI and digitalization will impact future drug treatment.
Many deep learning studies are not designed to provide unbiased estimation of the system's performance in the intended application. Reports of overoptimistic estimates and opportunities may inflate the expectation of what is currently possible, misguide resource allocation, and hamper the progression of the field. In this talk, we will look into how the performance of a deep learning system in an intended application could be estimated more reliably than what is currently common practice, even if restricted to using retrospective data.
The recent advances in deep learning and drastic increase in number of imaging satellites, new levels of automation are possible and necessary. KSAT is investing significantly into modern MLOps practices to achieve this and intends to use its membership in Visual Intelligence to solve the research aspects of this transformation.
Deep learning can bring time savings and increased reproducibility to medical image analysis. However, acquiring training data is challenging due to the time-intensive nature of labeling and high inter-observer variability in annotations. Rather than labeling images, in this work we propose an alternative pipeline where images are generated from existing high-quality annotations using generative adversarial networks (GANs). Annotations are derived automatically from previously built anatomical models and transformed into realistic synthetic ultrasound images with CycleGAN.
In fisheries acoustics, echo sounding is applied to detect fish and other marine objects in the ocean; a central tool for stock assessments and establishing fishing quotas. Fish detection and species classification from echo sounder data is typically a manual process. In our work, we automate this process by training a convolutional neural network for semantic segmentation using supervised learning. The talk will describe the data, the CNN-approach used for segmentation – as well as issues related to the training data, such as the quality of annotations when used in a machine learning setting.
Obtaining fully labeled datasets suitable for machine learning can be expensive, time-consuming, and impractical in many fields, limiting the applicability of the commonly used supervised approaches. Alba Ordoñes from NR presents their work with limited training data in the marine domain.
Benjamin Kellenberger from EPFL presents his work with limited training data in applications of unmanned aerial vehicles in earth observation to monitor wildlife. He presented his work "When a Few Clicks Make All the Difference: Improving Weakly-supervised Wildlife Detection in UAV Images", on the first Visual Intelligence Workshop on Limited training data.
Deep learning is the cornerstone of artificial intelligence applications across a wide range of tasks and domains. An important component that is missing from deep learning is explainability, i.e. the ability to explain what influenced a prediction made by a deep learning-based system. Explainable deep learning is an active area of research, with new algorithms being proposed at a rapid pace. This presentation highlights existing methods for explainable deep learning, as well as how to model uncertainty in explainability.
These two very interesing keynotes sets VI's work in the larger picture at the official opening of SFI Visual Intelligence. Professor Mark Girolami from the University of Cambridge and the Alan Turing Institute presents "From Data to Knowledge to Societal Impact and Value". Erik Steen (PhD) and Chief Engineer Cardiovascuar Ultrasound, GEVU presents "The Intelligent Ultrasound Scanner while giving a practical viewpoint of the innovation needs and possibilities within their domain of expertise.
The official opening of SFI Visual Intelligence with speeches and greetings by Director of Visual Intelligence Robert Jenssen, Rector of UiT Anne Husebekk, Director of UNN Anita Schumacher and many more.