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Computer Science

D-Index
44
Citations
8131
World Ranking
7575
National Ranking
454

Overview

Jens Rittscher is affiliated with the University of Oxford in the United Kingdom. The scientific work focuses primarily on fields such as Medicine, Biochemistry, Genetics and Molecular Biology, and Computer Science. Subfields of study include Oncology, Artificial Intelligence, Molecular Biology, Radiology, Nuclear Medicine and Imaging, as well as Computer Vision and Pattern Recognition.

The research topics frequently covered include AI in cancer detection, Colorectal Cancer Screening and Detection, Radiomics and Machine Learning in Medical Imaging, Cell Image Analysis Techniques, Myeloproliferative Neoplasms: Diagnosis and Treatment, Digital Imaging for Blood Diseases, and Acute Myeloid Leukemia Research.

Recent significant publications authored or co-authored by Jens Rittscher are:

  • Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning (2021) published in IEEE Access
  • Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning (2020) published in Gut
  • FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation (2022) published in IEEE Transactions on Neural Networks and Learning Systems
  • Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy (2021) published in Medical Image Analysis
  • A deep learning framework for quality assessment and restoration in video endoscopy (2020) published in Medical Image Analysis

Frequently collaborating co-authors include Sharib Ali, Korsuk Sirinukunwattana, Clare Verrill, James E. East, and Alan Aberdeen.

Prominent publication venues in which Jens Rittscher often publishes include arXiv (Cornell University), bioRxiv (Cold Spring Harbor Laboratory), Medical Image Analysis, Blood, and Zenodo (CERN European Organization for Nuclear Research).

Best Publications

  • Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue

    Michael J. Gerdes;Christopher J. Sevinsky;Anup Sood;Sudeshna Adak

  • Shape and Appearance Context Modeling

    Xiaogang Wang;G. Doretto;T. Sebastian;J. Rittscher

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

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

  • A Probabilistic Background Model for Tracking

    Jens Rittscher;Jien Kato;Sébastien Joga;Andrew Blake

  • A multi-objective supplier selection model under stochastic demand conditions

    Zhiying Liao;Jens Rittscher

  • Image-based consensus molecular subtype (imCMS) classification of colorectal cancer using deep learning

    Korsuk Sirinukunwattana;Enric Domingo;Susan D Richman;Keara L Redmond

  • Learning and classification of complex dynamics

    B. North;A. Blake;M. Isard;J. Rittscher

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

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

  • Appearance-based person reidentification in camera networks: problem overview and current approaches

    Gianfranco Doretto;Thomas Sebastian;Peter H. Tu;Jens Rittscher

  • Spatio-temporal cell cycle phase analysis using level sets and fast marching methods.

    Dirk R. Padfield;Dirk R. Padfield;Jens Rittscher;Nick Thomas;Badrinath Roysam

  • Simultaneous estimation of segmentation and shape

    J. Rittscher;P.H. Tu;N. Krahnstoever

  • Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis

    Dirk R. Padfield;Dirk R. Padfield;Jens Rittscher;Badrinath Roysam

  • Deep learning for detection and segmentation of artefact and disease instances in gastrointestinal endoscopy.

    Sharib Ali;Mariia Dmitrieva;Noha M. Ghatwary;Sophia Bano

  • An HMM-based segmentation method for traffic monitoring movies

    J. Kato;T. Watanabe;S. Joga;J. Rittscher

  • Surveillance systems and methods

    Timothy Patrick Kelliher;Jens Rittscher;Peter Henry Tu;Kevin Chean

  • Detecting and counting people in surveillance applications

    X. Liu;P.H. Tu;J. Rittscher;A. Perera

  • Precision immunoprofiling by image analysis and artificial intelligence.

    Viktor H. Koelzer;Korsuk Sirinukunwattana;Jens Rittscher;Jens Rittscher;Kirsten D. Mertz

  • Integration of supplier selection, procurement lot sizing and carrier selection under dynamic demand conditions

    Zhiying Liao;Jens Rittscher

  • A deep learning framework for quality assessment and restoration in video endoscopy.

    Sharib Ali;Felix Zhou;Adam Bailey;Barbara Braden

  • An objective comparison of detection and segmentation algorithms for artefacts in clinical endoscopy

    Sharib Ali;Felix Zhou;Barbara Braden;Adam Bailey

  • System and method for automatic person counting and detection of specific events

    Jens Rittscher;Peter Henry Tu;Nils Oliver Krahnstoever;Amitha Perera

Frequent Co-Authors

Peter Henry Tu
Peter Henry Tu General Electric (United States)
Andrew Blake
Andrew Blake University of Cambridge
Raghu Machiraju
Raghu Machiraju The Ohio State University
Xiaoming Liu
Xiaoming Liu University of North Carolina at Chapel Hill
Gustavo Leone
Gustavo Leone Medical University of South Carolina
Badrinath Roysam
Badrinath Roysam University of Houston
Xin Lu
Xin Lu University of Oxford
Ian Tomlinson
Ian Tomlinson University of Oxford
Daniel St Johnston
Daniel St Johnston University of Cambridge
David C. Wedge
David C. Wedge University of Manchester

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