World's Best Scientists 2026 revealed!
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Rising Stars
2025

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Rising Stars

D-Index
48
Citations
10203
World Ranking
361
National Ranking
3

Computer Science

D-Index
43
Citations
8104
World Ranking
7966
National Ranking
22

Research.com Recognitions

  • 2025 - Research.com Rising Stars Award

Overview

Michael Riegler is affiliated with OsloMet - Oslo Metropolitan University in Norway. Their research spans medicine and computer science, with a focus on subfields such as computer vision and pattern recognition, artificial intelligence, radiology, nuclear medicine and imaging, oncology, and public health, environmental and occupational health.

Their main research topics include radiomics and machine learning in medical imaging, colorectal cancer screening and detection, AI in cancer detection, reproductive biology and fertility, anomaly detection techniques and applications, artificial intelligence in healthcare and education, and gastric cancer management and outcomes.

Michael Riegler has published extensively, with frequent contributions to venues such as arXiv (Cornell University), Scientific Reports, IEEE Access, Human Reproduction, and Scientific Data.

  • arXiv (Cornell University)
  • Scientific Reports
  • IEEE Access
  • Human Reproduction
  • Scientific Data

Frequent co-authors include Pål Halvorsen, Steven A. Hicks, Vajira Thambawita, Hugo Lewi Hammer, and Debesh Jha.

  • Pål Halvorsen
  • Steven A. Hicks
  • Vajira Thambawita
  • Hugo Lewi Hammer
  • Debesh Jha

The scientist's recent papers include:

  • On evaluation metrics for medical applications of artificial intelligence, 2022, Scientific Reports
  • HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy, 2020, Scientific Data
  • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning, 2021, IEEE Access
  • A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation, 2021, IEEE Journal of Biomedical and Health Informatics
  • Metrics reloaded: recommendations for image analysis validation, 2024, Nature Methods

Michael Riegler's work often intersects areas of cancer detection and medical image analysis using AI techniques, with a significant emphasis on colorectal and gastric cancer screening technologies.

Best Publications

  • ResUNet++: An Advanced Architecture for Medical Image Segmentation

    Debesh Jha;Pia H. Smedsrud;Michael A. Riegler;Dag Johansen

  • Kvasir-SEG: A Segmented Polyp Dataset

    Debesh Jha;Pia H. Smedsrud;Michael A. Riegler;Pål Halvorsen

  • DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation

    Debesh Jha;Michael A. Riegler;Dag Johansen;Pal Halvorsen

  • KVASIR: A Multi-Class Image Dataset for Computer Aided Gastrointestinal Disease Detection

    Konstantin Pogorelov;Kristin Ranheim Randel;Carsten Griwodz;Sigrun Losada Eskeland

  • HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy.

    Hanna Borgli;Vajira Thambawita;Pia H Smedsrud;Steven Hicks

  • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning

    Debesh Jha;Sharib Ali;Nikhil Kumar Tomar;Havard D. Johansen

  • Mental health monitoring with multimodal sensing and machine learning: A survey

    Enrique Garcia-Ceja;Michael Riegler;Tine Nordgreen;Tine Nordgreen;Petter Jakobsen;Petter Jakobsen

  • A Comprehensive Study on Colorectal Polyp Segmentation With ResUNet++, Conditional Random Field and Test-Time Augmentation

    Debesh Jha;Pia H. Smedsrud;Dag Johansen;Thomas de Lange

  • Kvasir-Capsule, a video capsule endoscopy dataset.

    Pia H Smedsrud;Vajira Thambawita;Steven A Hicks;Henrik Gjestang

  • FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation.

    Nikhil Kumar Tomar;Debesh Jha;Michael A. Riegler;Håvard D. Johansen

  • Tiling in Interactive Panoramic Video: Approaches and Evaluation

    Vamsidhar Reddy Gaddam;Michael Riegler;Ragnhild Eg;Carsten Griwodz

  • Verifying Multimedia Use at MediaEval 2015

    Christina Boididou;Katerina Andreadou;Symeon Papadopoulos;Duc-Tien Dang-Nguyen

  • Natural disasters detection in social media and satellite imagery: a survey

    Naina Said;Kashif Ahmad;Michael Riegler;Konstantin Pogorelov

  • Impact of Image Resolution on Deep Learning Performance in Endoscopy Image Classification: An Experimental Study Using a Large Dataset of Endoscopic Images

    Vajira Thambawita;Inga Strümke;Steven A. Hicks;Pål Halvorsen

  • DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation

    Nikhil Kumar Tomar;Debesh Jha;Sharib Ali;Håvard D. Johansen

  • SinGAN-Seg: Synthetic Training Data Generation for Medical Image Segmentation

    Vajira Thambawita;Pegah Salehi;Sajad Amouei Sheshkal;Steven Alexander Hicks

  • ChaLearn Joint Contest on Multimedia Challenges Beyond Visual Analysis: An overview

    Hugo Jair Escalante;Victor Ponce-Lopez;Jun Wan;Michael A. Riegler

  • Comparing approaches to interactive lifelog search at the lifelog search challenge (LSC2018)

    Cathal Gurrin;Klaus Schoeffmann;Hideo Joho;Andreas Leibetseder

  • Depresjon: a motor activity database of depression episodes in unipolar and bipolar patients

    Enrique Garcia-Ceja;Michael Riegler;Petter Jakobsen;Jim Tørresen

  • ImageCLEF 2019: Multimedia Retrieval in Medicine, Lifelogging, Security and Nature

    Bogdan Ionescu;Henning Müller;Renaud Péteri;Yashin Dicente Cid

  • ResUNet++: An Advanced Architecture for Medical Image Segmentation

    Debesh Jha;Pia H. Smedsrud;Michael A. Riegler;Dag Johansen

  • DeepFake electrocardiograms using generative adversarial networks are the beginning of the end for privacy issues in medicine.

    Vajira Thambawita;Jonas L. Isaksen;Steven A. Hicks;Jonas Ghouse

  • Comparative validation of multi-instance instrument segmentation in endoscopy: Results of the ROBUST-MIS 2019 challenge

    Tobias Roß;Annika Reinke;Peter M. Full;Martin Wagner

  • Meta-learning with implicit gradients in a few-shot setting for medical image segmentation

    Unknown

  • Overview of ImageCLEF 2018: Challenges, Datasets and Evaluation

    Bogdan Ionescu;Henning Müller;Mauricio Villegas;Alba Garcia Seco de Herrera

  • Nerthus: A Bowel Preparation Quality Video Dataset

    Konstantin Pogorelov;Kristin Ranheim Randel;Thomas de Lange;Sigrun Losada Eskeland

  • On evaluation metrics for medical applications of artificial intelligence

    Steven A. Hicks;Inga Strümke;Vajira Thambawita;Malek Hammou

  • Verifying Multimedia Use at MediaEval 2016.

    Christina Boididou;Symeon Papadopoulos;Duc-Tien Dang-Nguyen;Giulia Boato

  • Kvasir-Instrument: Diagnostic and therapeutic tool segmentation dataset in gastrointestinal endoscopy

    Debesh Jha;Sharib Ali;Krister Emanuelsen;Steven A. Hicks

Frequent Co-Authors

Pål Halvorsen
Pål Halvorsen OsloMet – Oslo Metropolitan University
Dag Johansen
Dag Johansen University of Tromsø - The Arctic University of Norway
Carsten Griwodz
Carsten Griwodz University of Oslo
Cathal Gurrin
Cathal Gurrin Dublin City University
Martha Larson
Martha Larson Radboud University
Concetto Spampinato
Concetto Spampinato University of Catania
Jim Torresen
Jim Torresen University of Oslo
Ole Bernt Fasmer
Ole Bernt Fasmer University of Bergen
Henning Müller
Henning Müller University of Applied Sciences and Arts Western Switzerland
Niels Grarup
Niels Grarup University of Copenhagen

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