Search Results for author: Trygve Eftestøl

Found 6 papers, 1 papers with code

Activity Recognition From Newborn Resuscitation Videos

no code implementations14 Mar 2023 Øyvind Meinich-Bache, Simon Lennart Austnes, Kjersti Engan, Ivar Austvoll, Trygve Eftestøl, Helge Myklebust, Simeon Kusulla, Hussein Kidanto, Hege Ersdal

An important step is to generate timelines of relevant resuscitation activities, including ventilation, stimulation, suction, etc., during the resuscitation episodes.

Activity Recognition object-detection +1

Object Detection During Newborn Resuscitation Activities

no code implementations14 Mar 2023 Øyvind Meinich-Bache, Kjersti Engan, Ivar Austvoll, Trygve Eftestøl, Helge Myklebust, Ladislaus Blacy Yarrot, Hussein Kidanto, Hege Ersdal

Significance: This is the first step in a thorough analysis of newborn resuscitation episodes, which could provide important insight about the importance and effect of different newborn resuscitation activities

object-detection Object Detection

Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images

no code implementations9 Mar 2023 Saul Fuster, Farbod Khoraminia, Trygve Eftestøl, Tahlita C. M. Zuiverloon, Kjersti Engan

Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks.

Active Learning Domain Adaptation

3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI

no code implementations29 Dec 2022 Alvaro Fernandez-Quilez, Christoffer Gabrielsen Andersen, Trygve Eftestøl, Svein Reidar Kjosavik, Ketil Oppedal

Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images.

Lesion Classification Self-Supervised Learning

Nested Multiple Instance Learning with Attention Mechanisms

1 code implementation1 Nov 2021 Saul Fuster, Trygve Eftestøl, Kjersti Engan

Strongly supervised learning requires detailed knowledge of truth labels at instance levels, and in many machine learning applications this is a major drawback.

Multiple Instance Learning Time Series Analysis +1

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