English
norskBlar i forfatter "Østmo, Eirik Agnalt"
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A clinically motivated self-supervised approach for content-based image retrieval of CT liver images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-05-09)Deep learning-based approaches for content-based image retrieval (CBIR) of computed tomography (CT) liver images is an active field of research, but suffer from some critical limitations. First, they are heavily reliant on labeled data, which can be challenging and costly to acquire. Second, they lack transparency and explainability, which limits the trustworthiness of deep CBIR systems. We address ... -
A study of generative adversarial networks to improve classification of microscopic foraminifera
(Master thesis; Mastergradsoppgave, 2020-05-30)Foraminifera are single-celled organisms with shells that live in the marine environment and can be found abundantly as fossils in e.g. sediment cores. The assemblages of different species and their numbers serves as an important source of data for marine, geological, climate and environmental research. Steps towards automatic classification of foraminifera using deep learning (DL) models have ... -
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-23)Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in medical images. However, most deep learning models rely on augmentation techniques that treat medical images as natural images. For contrast-enhanced Computed Tomography (CT) images in particular, the signals producing the ...
