469 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of OuluFI13 papers
- Imperial College LondonGB3 papers
- KU LeuvenBE3 papers
- Masaryk UniversityCZ3 papers
- University Medical Center UtrechtNL3 papers
- Broad InstituteUS2 papers
- Centre Inria de SaclayFR2 papers
- Consejo Nacional de Investigaciones Científicas y TécnicasAR2 papers
- Deutsches Konsortium für Translationale KrebsforschungDE2 papers
- DKFZ-ZMBH AllianceDE2 papers
- Erasmus MCNL2 papers
- European Molecular Biology LaboratoryDE2 papers
13 papers
Complex Wavelet-Based Sinogram Segmentation for Metal Artifact Reduction in Cone-Beam CT
Siiri Rautio, Alexander Meaney, Salla-Maaria Latva-Äijö +4
Metal objects pose a significant challenge in cone-beam computed tomography, as their strong and energy-dependent X-ray attenuation leads to inconsistent projections and severe str…
Understanding metric-related pitfalls in image analysis validation
Annika Reinke, Minu D. Tizabi, Michael Baumgartner +75
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation int…
Domain-specific transfer learning in the automated scoring of tumor-stroma ratio from histopathological images of colorectal cancer
Liisa Petäinen, Juha P. Väyrynen, Pekka Ruusuvuori +3
Tumor-stroma ratio (TSR) is a prognostic factor for many types of solid tumors. In this study, we propose a method for automated estimation of TSR from histopathological images of…
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data
Huy Hoang Nguyen, Matthew B. Blaschko, Simo Saarakkala +1
Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face…
Metrics reloaded: Recommendations for image analysis validation
Lena Maier-Hein, Annika Reinke, Patrick Godau +71
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen…
Predicting Knee Osteoarthritis Progression from Structural MRI using Deep Learning
Egor Panfilov, Simo Saarakkala, Miika T. Nieminen +1
Accurate prediction of knee osteoarthritis (KOA) progression from structural MRI has a potential to enhance disease understanding and support clinical trials. Prior art focused on…
Non-contact Atrial Fibrillation Detection from Face Videos by Learning Systolic Peaks
Zhaodong Sun, Juhani Junttila, Mikko Tulppo +2
Objective: We propose a non-contact approach for atrial fibrillation (AF) detection from face videos. Methods: Face videos, electrocardiography (ECG), and contact photoplethysmogra…
Present criteria for prophylactic ICD implantation: Insights from the EU-CERT-ICD (Comparative Effectiveness Research to Assess the Use of Primary ProphylacTic Implantable Cardioverter Defibrillators in EUrope) project
M. Zabel, S. Schlögl, A. Lubinski +45
BACKGROUND. The clinical effectiveness of primary prevention implantable cardioverter defibrillator (ICD) therapy is under debate. It is urgently needed to better identify patients…
Adaptive Segmentation of Knee Radiographs for Selecting the Optimal ROI in Texture Analysis
Neslihan Bayramoglu, Aleksei Tiulpin, Jukka Hirvasniemi +2
The purposes of this study were to investigate: 1) the effect of placement of region-of-interest (ROI) for texture analysis of subchondral bone in knee radiographs, and 2) the abil…
Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation
Egor Panfilov, Aleksei Tiulpin, Stefan Klein +2
Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is…
Bone Density and Texture from Minimally Post-Processed Knee Radiographs in Subjects with Knee Osteoarthritis
Jukka Hirvasniemi, Jaakko Niinimäki, Jérôme Thevenot +1
Plain radiography is the most common modality to assess the stage of osteoarthritis. Our aims were to assess the relationship of radiography-based bone density and texture between…
A novel method for automatic localization of joint area on knee plain radiographs
Aleksei Tiulpin, Jérôme Thevenot, Esa Rahtu +1
Osteoarthritis (OA) is a common musculoskeletal condition typically diagnosed from radiographic assessment after clinical examination. However, a visual evaluation made by a practi…
Bayesian Information Sharing Between Noise And Regression Models Improves Prediction of Weak Effects
Jussi Gillberg, Pekka Marttinen, Matti Pirinen +5
We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than , of the variance of…