Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation
Konstantinos Kamnitsas,Wenjia Bai,Enzo Ferrante,Steven McDonagh,Matthew Sinclair,Nick Pawlowski,Martin Rajchl,Matthew C. H. Lee,Bernhard Kainz,Daniel Rueckert,Ben Glocker +10 more
- 14 Sep 2017
- pp 450-462
382
TL;DR: The Ensembles of Multiple Models and Architectures (EMMA) as mentioned in this paper was proposed to reduce the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database.
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Abstract: Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.
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Citations
Automatic Brain Tumor Segmentation by Exploring the Multi-modality Complementary Information and Cascaded 3D Lightweight CNNs
Jun Ma,Xiaoping Yang +1 more
- 16 Sep 2018
TL;DR: A mechanism is developed by training the CNNs like the annotation process by radiologists to improve the brain tumor segmentation accuracy compared with the common merging strategy and a 3D lightweight CNN is proposed to extract brain tumor substructures.
MVP U-Net: Multi-View Pointwise U-Net for Brain Tumor Segmentation
Changchen Zhao,Zhao Zhiming,Qingrun Zeng,Yuanjing Feng +3 more
- 04 Oct 2020
TL;DR: Zhang et al. as mentioned in this paper proposed Multi-View Pointwise U-Net (MVPU-Net) for brain tumor segmentation, where the 3D convolution is replaced by three 2D multi-view convolutions in three orthogonal views (axial, sagittal, coronal) of the input data to learn spatial features and one pointwise convolution to learn channel features.
Knowledge distillation with ensembles of convolutional neural networks for medical image segmentation
28 May 2022
TL;DR: In this paper , the authors compared the performance of different types of convolutional neural networks (CNNs) in medical image segmentation tasks using knowledge distillation, a technique for reducing the footprint of large models such as ensembles.
Attention-Guided Version of 2D UNet for Automatic Brain Tumor Segmentation
Mehrdad Noori,Ali Bahri,Karim Mohammadi +2 more
- 01 Oct 2019
TL;DR: A low-parameter network based on 2D UNet in which an attention mechanism is adopted after concatenation of low-level and high-level features and the Multi-View Fusion can benefit from 3D contextual information of input images despite using a 2D model.
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Prediction of Thrombectomy Functional Outcomes using Multimodal Data
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References
U-Net: Convolutional Networks for Biomedical Image Segmentation
Olaf Ronneberger,Philipp Fischer,Thomas Brox +2 more
- 05 Oct 2015
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Fully convolutional networks for semantic segmentation
Jonathan Long,Evan Shelhamer,Trevor Darrell +2 more
- 07 Jun 2015
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Multilayer feedforward networks are universal approximators
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
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Bagging predictors
Leo Breiman
- 01 Aug 1996
TL;DR: Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy.
The 2016 World Health Organization Classification of Tumors of the Central Nervous System: a summary.
David N. Louis,Arie Perry,Guido Reifenberger,Andreas von Deimling,Dominique Figarella-Branger,Webster K. Cavenee,Hiroko Ohgaki,Otmar D. Wiestler,Paul Kleihues,David W. Ellison +9 more
TL;DR: The 2016 World Health Organization Classification of Tumors of the Central Nervous System is both a conceptual and practical advance over its 2007 predecessor and is hoped that it will facilitate clinical, experimental and epidemiological studies that will lead to improvements in the lives of patients with brain tumors.
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