Medical-based Deep Curriculum Learning for Improved Fracture Classification
Jimenez-Sanchez A, Mateus D, Kirchhoff S, Kirchhoff C, Biberthaler P, Navab N, Gonzalez Ballester MA, Piella G (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11769 LNCS
Pages Range: 694-702
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322250
DOI: 10.1007/978-3-030-32226-7_77
Abstract
Current deep-learning based methods do not easily integrate to clinical protocols, neither take full advantage of medical knowledge. In this work, we propose and compare several strategies relying on curriculum learning, to support the classification of proximal femur fracture from X-ray images, a challenging problem as reflected by existing intra- and inter-expert disagreement. Our strategies are derived from knowledge such as medical decision trees and inconsistencies in the annotations of multiple experts, which allows us to assign a degree of difficulty to each training sample. We demonstrate that if we start learning “easy” examples and move towards “hard”, the model can reach a better performance, even with fewer data. The evaluation is performed on the classification of a clinical dataset of about 1000 X-ray images. Our results show that, compared to class-uniform and random strategies, the proposed medical knowledge-based curriculum, performs up to 15% better in terms of accuracy, achieving the performance of experienced trauma surgeons.
Involved external institutions
Universitat Pompeu Fabra (UPF)
Spain (ES)
École Centrale de Nantes (ECN)
France (FR)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Spain (ES)
École Centrale de Nantes (ECN)
France (FR)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
How to cite
APA:
Jimenez-Sanchez, A., Mateus, D., Kirchhoff, S., Kirchhoff, C., Biberthaler, P., Navab, N.,... Piella, G. (2019). Medical-based Deep Curriculum Learning for Improved Fracture Classification. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 694-702). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Jimenez-Sanchez, Amelia, et al. "Medical-based Deep Curriculum Learning for Improved Fracture Classification." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 694-702.
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