Error corrective boosting for learning fully convolutional networks with limited data
Roy AG, Conjeti S, Sheet D, Katouzian A, Navab N, Wachinger C (2017)
Publication Type: Conference contribution
Publication year: 2017
Journal
Publisher: Springer Verlag
Book Volume: 10435 LNCS
Pages Range: 231-239
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Quebec City, QC, CAN
ISBN: 9783319661780
DOI: 10.1007/978-3-319-66179-7_27
Abstract
Training deep fully convolutional neural networks (F-CNNs) for semantic image segmentation requires access to abundant labeled data. While large datasets of unlabeled image data are available in medical applications, access to manually labeled data is very limited. We propose to automatically create auxiliary labels on initially unlabeled data with existing tools and to use them for pre-training. For the subsequent fine-tuning of the network with manually labeled data, we introduce error corrective boosting (ECB), which emphasizes parameter updates on classes with lower accuracy. Furthermore, we introduce SkipDeconv-Net (SD-Net), a new F-CNN architecture for brain segmentation that combines skip connections with the unpooling strategy for upsampling. The SD-Net addresses challenges of severe class imbalance and errors along boundaries. With application to whole-brain MRI T1 scan segmentation, we generate auxiliary labels on a large dataset with FreeSurfer and fine-tune on two datasets with manual annotations. Our results show that the inclusion of auxiliary labels and ECB yields significant improvements. SD-Net segments a 3D scan in 7 s in comparison to 30 h for the closest multi-atlas segmentation method, while reaching similar performance. It also outperforms the latest state-of-the-art F-CNN models.
Involved external institutions
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Indian Institute of Technology Kharagpur
India (IN)
IBM Research - Almaden
United States (USA) (US)
Germany (DE)
Technische Universität München (TUM)
Germany (DE)
Indian Institute of Technology Kharagpur
India (IN)
IBM Research - Almaden
United States (USA) (US)
How to cite
APA:
Roy, A.G., Conjeti, S., Sheet, D., Katouzian, A., Navab, N., & Wachinger, C. (2017). Error corrective boosting for learning fully convolutional networks with limited data. In Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 231-239). Quebec City, QC, CAN: Springer Verlag.
MLA:
Roy, Abhijit Guha, et al. "Error corrective boosting for learning fully convolutional networks with limited data." Proceedings of the 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2017, Quebec City, QC, CAN Ed. Lena Maier-Hein, Alfred Franz, Pierre Jannin, Simon Duchesne, Maxime Descoteaux, D. Louis Collins, Springer Verlag, 2017. 231-239.
BibTeX: Download