Extending pretrained segmentation networks with additional anatomical structures.

Purpose For comprehensive surgical planning with sophisticated patient-specific models, all relevant anatomical structures need to be segmented. This could be achieved using deep neural networks given sufficiently many annotated samples; however, datasets of multiple annotated structures are often unavailable in practice and costly to procure. Therefore, being able to build segmentation models with datasets from different studies and centers in an incremental fashion is highly desirable.

[1]  Cordelia Schmid,et al.  End-to-End Incremental Learning , 2018, ECCV.

[2]  Philipp Fürnstahl,et al.  Learn the new, keep the old: Extending pretrained models with new anatomy and images , 2018, MICCAI.

[3]  Orcun Goksel,et al.  Overview of the VISCERAL Challenge at ISBI 2015 , 2015, VISCERAL Challenge@ISBI.

[4]  Orcun Goksel,et al.  Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy , 2018, DLMIA/ML-CDS@MICCAI.

[5]  Michael McCloskey,et al.  Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem , 1989 .

[6]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

[7]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[8]  Marc Pollefeys,et al.  An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation , 2017, STACOM@MICCAI.

[9]  Yann Dauphin,et al.  Empirical Analysis of the Hessian of Over-Parametrized Neural Networks , 2017, ICLR.

[10]  Dorit S. Hochbaum,et al.  Approximation Algorithms for NP-Hard Problems , 1996 .

[11]  Lin Yang,et al.  Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation , 2017, MICCAI.

[12]  Geoffrey E. Hinton,et al.  Distilling the Knowledge in a Neural Network , 2015, ArXiv.

[13]  Zoubin Ghahramani,et al.  Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning , 2015, ICML.

[14]  Christoph H. Lampert,et al.  iCaRL: Incremental Classifier and Representation Learning , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).