Annotation Lab Essen

Creating a high-quality dataset is a crucial part of any machine learning project. This is even more the case for highly specialized domains such as medicine. In practice, data annotation often takes longer than the actual training and optimization of the models. Medical data annotation includes several process steps: planning, anonymization, provision of the data, annotation/segmentation, and quality assurance. Annotation Lab Essen, an organizational unit of the Institute for AI in Medicine and part of Essen University Hospital, benefits from the highest medical expertise and specializes in the annotation of medical data (images, text and beyond).

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Evaluation

Evaluation

Evaluation and Reader Studies of AI algorithms (e.g. on bone age estimation)

FHIR Query & Anonymization

FHIR Query & Anonymization

Semi-automated pipeline for medical text anonymization for consecutive text annotation

Histopathology

Histopathology

Semi-automatic annotation of histopathological images

Image Segmentation

Image Segmentation

Segmentation of pulmonary pathologies of COVID-19 patients' image

Structured Report

Structured Report

Pipeline for generation of structured reports from unstructured radiological data

Teaching-learning material

Teaching-learning material

SOP of Body-Composition Analysis and integration of ontology definitions

Chair

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Felix Nensa

Professor of Data Integration and AI in Radiology

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Jens Kleesiek

Professor of Translational Image-guided Oncology

Team

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Cynthia Schmidt

Physician

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Gordon Meyer

Project Management

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Kelsey Herrmann

Radiologist

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Lennard Kroll

Radiologist

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Moon Kim

Team Lead

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Natalie van Landeghem

Radiologist

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Alina Krenzin

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Anton Konrad Schily

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Carolina Steinrücke

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Daniel van Landeghem

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Enver Deniz

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Hannah Sievert

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Irina Kallmeyer

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Johanna Märzheuser

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Katharina Barbara Abel

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Lasse Löffler

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Olivia Pollok

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Pia Nath

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Sara Erma Kaya

Partners

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