23 citations · 98 across the 22 of their papers we have counts for
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NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
Saul Fuster, Umay Kiraz, Trygve Eftestøl +2
The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime…
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
Neel Kanwal, Farbod Khoraminia, Umay Kiraz +6
Histopathology is a gold standard for cancer diagnosis under a microscopic examination. However, histological tissue processing procedures result in artifacts, which are ultimately…
A Dual Convolutional Neural Network Pipeline for Melanoma Diagnostics and Prognostics
Marie Bø-Sande, Edvin Benjaminsen, Neel Kanwal +5
Melanoma is a type of cancer that begins in the cells controlling the pigment of the skin, and it is often referred to as the most dangerous skin cancer. Diagnosing melanoma can be…
Self-supervised learning of a tailored Convolutional Auto Encoder for histopathological prostate grading
Zahra Tabatabaei, Adrian colomer, Kjersti Engan +2
According to GLOBOCAN 2020, prostate cancer is the second most common cancer in men worldwide and the fourth most prevalent cancer overall. For pathologists, grading prostate cance…
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
Luca Tomasetti, Stine Hansen, Mahdieh Khanmohammadi +4
Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest morta…
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…
Deep Learning for Predicting Metastasis on Melanoma WSIs
Christopher Andreassen, Saul Fuster, Helga Hardardottir +2
Northern Europe has the second highest mortality rate of melanoma globally. In 2020, the mortality rate of melanoma rose to 1.9 per 100 000 habitants. Melanoma prognosis is based o…
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,…
CT Perfusion is All We Need: 4D CNN Segmentation of Penumbra and Core in Patients With Suspected Ischemic Stroke
Luca Tomasetti, Kjersti Engan, Liv Jorunn Høllesli +2
Precise and fast prediction methods for ischemic areas comprised of dead tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS) patients are of significant…
Detection and Localization of Melanoma Skin Cancer in Histopathological Whole Slide Images
Neel Kanwal, Roger Amundsen, Helga Hardardottir +4
Melanoma diagnosed and treated in its early stages can increase the survival rate. A projected increase in skin cancer incidents and a dearth of dermatopathologists have emphasized…
Multi-input segmentation of damaged brain in acute ischemic stroke patients using slow fusion with skip connection
Luca Tomasetti, Mahdieh Khanmohammadi, Kjersti Engan +2
Time is a fundamental factor during stroke treatments. A fast, automatic approach that segments the ischemic regions helps treatment decisions. In clinical use today, a set of colo…
CNN Based Segmentation of Infarcted Regions in Acute Cerebral Stroke Patients From Computed Tomography Perfusion Imaging
Luca Tomasetti, Kjersti Engan, Mahdieh Khanmohammadi +1
More than 13 million people suffer from ischemic cerebral stroke worldwide each year. Thrombolytic treatment can reduce brain damage but has a narrow treatment window. Computed Tom…
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…