Andreas Nürnberger
researcher, ORCID id # 0000-0003-4311-0624
Facts
6 properties- Instance of
- human
- Date of birth
- 1968
- Occupation
- researcher, university teacher, computer scientist
- Employer
- University of Magdeburg[5]
🏷️ Also known as
en Andreas Nürnberger
🔖 Identifiers · 16
- ISNI
- 0000000121315165
- ORCID iD
- 0000-0003-4311-0624
- VIAF cluster ID
- 47660162
- Library of Congress authority ID
- nb2004301328
- GND ID
- 123076846
- ACM Digital Library author ID
- 81100529724
- DBLP author ID
- 04/1237
- DDB person (GND) ID
- 123076846
- Dimensions author ID
- 012475611153.05
- IdRef ID
- 081542828
- National Library of Israel J9U ID
- 987007444357105171
- National Library of Poland MMS ID
- 9810574721305606
Show all 16 identifiers +4
- Nationale Thesaurus voor Auteursnamen ID
- 266064973
- Scopus author ID
- 14027288100
- WorldCat Entities ID
- E39PBJdgYXTqg9MCRrXr8k3WjC
- Yale LUX ID
- person/23749792-6f39-42c1-a16c-320f8e8ba554
🔗 Connections
Author of 13
CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation,
Classification of brain tumours in MR images using deep spatiospatial models,
Contemporary Amharic Corpus: Automatically Morpho-Syntactically Tagged Amharic Corpus,
DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data,
Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images,
German Speech Recognition System using DeepSpeech,
<i>MedShapeNet</i> – a large-scale dataset of 3D medical shapes for computer vision,
Improving Automatic Speech Recognition Utilizing Audio-codecs for Data Augmentation
+5 more
← Authored by 13
CHAOS Challenge - combined (CT-MR) healthy abdominal organ segmentation,
Classification of brain tumours in MR images using deep spatiospatial models,
Contemporary Amharic Corpus: Automatically Morpho-Syntactically Tagged Amharic Corpus,
DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data,
Exploration of Interpretability Techniques for Deep COVID-19 Classification Using Chest X-ray Images,
German Speech Recognition System using DeepSpeech,
<i>MedShapeNet</i> – a large-scale dataset of 3D medical shapes for computer vision,
Improving Automatic Speech Recognition Utilizing Audio-codecs for Data Augmentation
+5 more
Affiliated with 3
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