23 citations · 98 across the 22 of their papers we have counts for
24 papers
Bi-temporal Image-driven Acute Stroke Evolution Analysis
Md Sazidur Rahman, Kjersti Engan, Kathinka Dæhli Kurz +1
Acute ischemic stroke requires rapid treatment decisions that are strongly guided by emergency imaging. Admission computed tomography perfusion (CTP) is commonly used to estimate t…
FHRFormer: A Self-supervised Transformer Approach for Fetal Heart Rate Inpainting and Forecasting
Kjersti Engan, Neel Kanwal, Anita Yeconia +4
Approximately 10\% of newborns require assistance to initiate breathing at birth, and around 5\% need ventilation support. Fetal heart rate (FHR) monitoring plays a crucial role in…
Advancing Newborn Care: Precise Birth Time Detection Using AI-Driven Thermal Imaging with Adaptive Normalization
Jorge García-Torres, Øyvind Meinich-Bache, Anders Johannessen +3
Around 5-10\% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from…
PriCE: Privacy-Preserving and Cost-Effective Scheduling for Parallelizing the Large Medical Image Processing Workflow over Hybrid Clouds
Yuandou Wang, Neel Kanwal, Kjersti Engan +3
Running deep neural networks for large medical images is a resource-hungry and time-consuming task with centralized computing. Outsourcing such medical image processing tasks to hy…
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…
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
Saul Fuster, Farbod Khoraminia, Julio Silva-Rodríguez +7
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated pro…
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…
Towards Privacy-, Budget-, and Deadline-Aware Service Optimization for Large Medical Image Processing across Hybrid Clouds
Yuandou Wang, Neel Kanwal, Kjersti Engan +3
Efficiently processing medical images, such as whole slide images in digital pathology, is essential for timely diagnosing high-risk diseases. However, this demands advanced comput…
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…
Balancing Privacy and Progress in Artificial Intelligence: Anonymization in Histopathology for Biomedical Research and Education
Neel Kanwal, Emiel A. M. Janssen, Kjersti Engan
The advancement of biomedical research heavily relies on access to large amounts of medical data. In the case of histopathology, Whole Slide Images (WSI) and clinicopathological in…
Towards a privacy-preserving distributed cloud service for preprocessing very large medical images
Yuandou Wang, Neel Kanwal, Kjersti Engan +2
Digitized histopathology glass slides, known as Whole Slide Images (WSIs), are often several gigapixels large and contain sensitive metadata information, which makes distributed pr…
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…
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…
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…
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…
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…
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…