Matthias Eisenmann

Orcid: 0000-0002-0713-8761

According to our database1, Matthias Eisenmann authored at least 20 papers between 2014 and 2023.

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Bibliography

2023
Why is the winner the best?
CoRR, 2023

Understanding metric-related pitfalls in image analysis validation.
CoRR, 2023

Application of the Metaverse in Product Engineering - A Workshop for Identification of Potential Field of Action.
Proceedings of the Metaverse - METAVERSE 2023, 2023

Why is the Winner the Best?
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Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023

2022
A Delphi consensus statement for digital surgery.
npj Digit. Medicine, 2022

Surgical data science - from concepts toward clinical translation.
Medical Image Anal., 2022

Biomedical image analysis competitions: The state of current participation practice.
CoRR, 2022

Metrics reloaded: Pitfalls and recommendations for image analysis validation.
CoRR, 2022

Abstract: How to Generate Patient Benefit with Surgical Data Science - Results of an International Delphi Process.
Proceedings of the Bildverarbeitung für die Medizin 2022, 2022

2021
Common Limitations of Image Processing Metrics: A Picture Story.
CoRR, 2021

2020
BIAS: Transparent reporting of biomedical image analysis challenges.
Medical Image Anal., 2020

Surgical Data Science - from Concepts to Clinical Translation.
CoRR, 2020

Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room.
CoRR, 2020

2018
Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions.
Dataset, June, 2018

Surgical Data Science: A Consensus Perspective.
CoRR, 2018

Is the winner really the best? A critical analysis of common research practice in biomedical image analysis competitions.
CoRR, 2018


2017
Surgical Data Science: Enabling Next-Generation Surgery.
CoRR, 2017

Abstract: Können wir Rankings vertrauen? Eine systematische Analyse biomedizinischer Challenges hinsichtlich Reporting und Design.
Proceedings of the Bildverarbeitung für die Medizin 2017 - Algorithmen - Systeme, 2017

2014
Can Masses of Non-Experts Train Highly Accurate Image Classifiers? - A Crowdsourcing Approach to Instrument Segmentation in Laparoscopic Images.
Proceedings of the Medical Image Computing and Computer-Assisted Intervention - MICCAI 2014, 2014


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