Projects per year
Abstract
Age estimation from radiologic data is an important topic in forensic medicine to assess chronological age or to discriminate minors from adults, e.g. asylum seekers lacking valid identification documents. In this work we propose automatic multi-factorial age estimation methods based on MRI data to extend the maximal age range from 19 years, as commonly used for age assessment based on hand bones, up to 25 years, when combined with wisdom teeth and clavicles. Mimicking how radiologists perform age estimation, our proposed method based on deep convolutional neural networks achieves a result of 1.14 \pm 0.96 years of mean absolute error in predicting chronological age. Further, when fine-tuning the same network for majority age classification, we show an improvement in sensitivity of the multi-factorial system compared to solely relying on the hand.
Original language | English |
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Title of host publication | Machine Learning in Medical Imaging - 8th International Workshop, MLMI 2017, Held in Conjunction with MICCAI 2017, Proceedings |
Publisher | Springer-Verlag Italia |
Pages | 61-69 |
Number of pages | 9 |
Volume | 10541 LNCS |
ISBN (Print) | 9783319673882 |
DOIs | |
Publication status | Published - 2017 |
Event | 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017: MLMI 2017 / MICCAI 2017 - Quebec City, Canada Duration: 10 Sept 2017 → 10 Sept 2017 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10541 LNCS |
ISSN (Print) | 03029743 |
ISSN (Electronic) | 16113349 |
Conference
Conference | 8th International Workshop on Machine Learning in Medical Imaging, MLMI 2017 held in conjunction with the 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 |
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Country/Territory | Canada |
City | Quebec City |
Period | 10/09/17 → 10/09/17 |
Keywords
- Convolutional neural network
- Forensic age estimation
- Information fusion
- Multi-factorial method
- Random forest
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Fields of Expertise
- Information, Communication & Computing
Cooperations
- BioTechMed-Graz
Projects
- 1 Finished
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FWF - FAME - Fully Automatic MRI-based Age Estimation of Adolescents
Bischof, H. & Urschler, M.
1/07/15 → 31/12/18
Project: Research project