23 citations · 44 across the 9 of their papers we have counts for
11 papers
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
Tim Nikolass Lindeijer, Tord Martin Ytredal, Trygve Eftestøl +4
An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools conside…
On undesired emergent behaviors in compound prostate cancer detection systems
Erlend Sortland Rolfsnes, Philip Thangngat, Trygve Eftestøl +4
Artificial intelligence systems show promise to aid in the di- agnostic pathway of prostate cancer (PC), by supporting radiologists in interpreting magnetic resonance images (MRI)…
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection
Alvaro Fernandez-Quilez, Linas Vidziunas, Ørjan Kløvfjell Thoresen +3
Traditional deep learning (DL) approaches based on supervised learning paradigms require large amounts of annotated data that are rarely available in the medical domain. Unsupervis…
Vision Transformers for Small Histological Datasets Learned through Knowledge Distillation
Neel Kanwal, Trygve Eftestol, Farbod Khoraminia +2
Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks. However, the artifacts on the digitized histological glass slides, known as Whole Slide Ima…
Object Detection During Newborn Resuscitation Activities
Øyvind Meinich-Bache, Kjersti Engan, Ivar Austvoll +5
Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations; however, the importance and effect of the…
Activity Recognition From Newborn Resuscitation Videos
Øyvind Meinich-Bache, Simon Lennart Austnes, Kjersti Engan +6
Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of rec…
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images
Saul Fuster, Farbod Khoraminia, Trygve Eftestøl +2
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still,…
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI
Alvaro Fernandez-Quilez, Christoffer Gabrielsen Andersen, Trygve Eftestøl +2
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 o…
Nested Multiple Instance Learning with Attention Mechanisms
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 l…
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI
Alvaro Fernandez-Quilez, Trygve Eftestøl, Morten Goodwin +2
Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to…
Multiscale Deep Neural Networks for Multiclass Tissue Classification of Histological Whole-Slide Images
Rune Wetteland, Kjersti Engan, Trygve Eftestøl +2
Correct treatment of urothelial carcinoma patients is dependent on accurate grading and staging of the cancer tumour. This is determined manually by a pathologist by examining the…