Jens Petersen
A5101699990- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Radiomics and Machine Learning in Medical Imaging
- Quantum Chromodynamics and Particle Interactions
- Particle Detector Development and Performance
- Medical Image Segmentation Techniques
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Lung Cancer Diagnosis and Treatment
- COVID-19 diagnosis using AI
- Anomaly Detection Techniques and Applications
- Generative Adversarial Networks and Image Synthesis
- Advanced Neural Network Applications
- AI in cancer detection
- Computational Physics and Python Applications
- Smart Agriculture and AI
- Cell Image Analysis Techniques
- Machine Learning in Healthcare
- Brain Tumor Detection and Classification
- Distributed and Parallel Computing Systems
- Advanced Radiotherapy Techniques
- Medical Imaging Techniques and Applications
- Interstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
- Neonatal Respiratory Health Research
- Single-cell and spatial transcriptomics
- Surgical Simulation and Training
Heidelberg University
2016-2025
German Cancer Research Center
2017-2025
University of Copenhagen
2016-2025
Rigshospitalet
2021-2025
Qualcomm (United Kingdom)
2023-2024
Copenhagen University Hospital
2022
Sygehus Sønderjylland
2019-2022
University of Würzburg
2022
Hospital South West Jutland
2019-2021
University Hospital Heidelberg
2017-2020
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
The U-Net was presented in 2015. With its straight-forward and successful architecture it quickly evolved to a commonly used benchmark medical image segmentation. adaptation of the novel problems, however, comprises several degrees freedom regarding exact architecture, preprocessing, training inference. These choices are not independent each other substantially impact overall performance. present paper introduces nnU-Net ('no-new-Net'), which refers robust self-adapting framework on basis 2D...
In this work, we report the set-up and results of Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI) 2017 Conferences Medical Image Computing Computer-Assisted Intervention (MICCAI) 2018. The image dataset is diverse contains primary secondary tumors varied sizes appearances various lesion-to-background levels (hyper-/hypo-dense), created collaboration seven hospitals research institutions. Seventy-five...
Summary Convolutional neural networks (CNNs) are a powerful tool for plant image analysis, but challenges remain in making them more accessible to researchers without machine‐learning background. We present R oot P ainter , an open‐source graphical user interface based software the rapid training of deep use biological analysis. evaluate by models root length extraction from chicory ( Cichorium intybus L.) roots soil, biopore counting, and nodule counting. also compare dense annotations with...
Abstract Background Plant root research can provide a way to attain stress-tolerant crops that produce greater yield in diverse array of conditions. Phenotyping roots soil is often challenging due the being difficult access and use time consuming manual methods. Rhizotrons allow visual inspection growth through transparent surfaces. Agronomists currently manually label photographs obtained from rhizotrons using line-intersect method obtain length density rooting depth measurements which are...
Abstract Patients with severe COVID-19 have overwhelmed healthcare systems worldwide. We hypothesized that machine learning (ML) models could be used to predict risks at different stages of management and thereby provide insights into drivers prognostic markers disease progression death. From a cohort approx. 2.6 million citizens in Denmark, SARS-CoV-2 PCR tests were performed on subjects suspected for disease; 3944 cases had least one positive test subjected further analysis. from the...
Unsupervised learning can leverage large-scale data sources without the need for annotations. In this context, deep learning-based auto encoders have shown great potential in detecting anomalies medical images. However, state-of-the-art anomaly scores are still based on reconstruction error, which lacks two essential parts: it ignores model-internal representation employed reconstruction, and formal assertions comparability between samples. We address these shortcomings by proposing...
To quantify airway and artery (AA)-dimensions in cystic fibrosis (CF) control patients for objective CT diagnosis of bronchiectasis wall thickness (AWT).Spirometer-guided inspiratory expiratory CTs 11 CF 12 were collected retrospectively. Airway pathways annotated semi-automatically to reconstruct three-dimensional bronchial trees. All visible AA-pairs measured perpendicular the axis. Inner, outer AWT (outer-inner) diameter divided by adjacent compute AinA-, AoutA- AWTA-ratios. AA-ratios...
In this prospective nationwide multicenter study from Denmark, myopericarditis after Pfizer-BioNTech mRNA COVID-19 vaccination was identified in 13 males and 2 females between May 15 September 15, 2021, among 133,477 vaccinated 127,857 12–17 years of age, equaling 97 16 per million. conclusion, the incidence appears higher than reports United States.
The authors have no conflicts of interest to declare.
Abstract Background To assess whether artificial intelligence (AI)-based decision support allows more reproducible and standardized assessment of treatment response on MRI in neuro-oncology as compared to manual 2-dimensional measurements tumor burden using the Response Assessment Neuro-Oncology (RANO) criteria. Methods A series 30 patients (15 lower-grade gliomas, 15 glioblastoma) with availability consecutive scans was selected. The time progression (TTP) separately evaluated for each...
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus failing to adequately measure scientific progress and hindering translation of ML techniques into practice. To overcome this, our large international expert consortium created Metrics Reloaded, a comprehensive framework guiding researchers...
Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment machine learning algorithms medicine. When encounter cases that deviate from distribution training data, they often produce incorrect over-confident predictions. OoD detection aim to catch erroneous predictions advance by analysing detecting potential instances failure. Moreover, flagging may support human readers identifying incidental findings. Due increased interest algorithms,...
Abstract Background In minimally invasive surgery (MIS), trainees need to learn how interpret the operative field displayed on laparoscopic screen. Experts currently guide mainly verbally during procedures. A newly developed telestration system with augmented reality (iSurgeon) allows instructor display hand gestures in real-time screen provide visual expert guidance (telestration). This study analysed effect of guided instructions gaze behaviour MIS training. Methods a randomized-controlled...