The purpose of this paper is to present multi-organ segmentation method using spatial information-embedded fully convolutional networks (FCNs). Semantic segmentation of major anatomical structure from CT volumes is promising to apply in clinical work ows. A multitude of deep-learning-based approaches have been proposed for 3D image processing.
BibTeX @MISC{Pinheiro14weaklysupervised, author = {Pedro H. O. Pinheiro and Ronan Collobert and Pedro O. Pinheiro and Ronan Collobert}, title = {Weakly Supervised Object Segmentation with Convolutional Neural Networks}, year = {2014}}
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bibtex. This paper is subsumed by SIRFS. Discovering Efficiency in Coarse-To-Fine Texture Classification Jonathan T. Barron, Jitendra Malik Technical Report, 2010 bibtex. A model and feature representation that allows for sub-linear coarse-to-fine semantic segmentation. Parallelizing Reinforcement Learning

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  • Automatic Building Roof Extraction with Fully Convolutional Neural Network LIU Wentao 1 (), LI Shihua 1, QIN Yuchu 2, * () 1. School of Resources and ...
  • Multi-task Layout Analysis for Historical Handwritten Documents Using Fully Convolutional Networks. International Joint Conference on Artificial Intelligence (IJCAI). [pdf][bibtex] Liming Zhao, Mingjie Li, Depu Meng, Xi Li, Zhaoxiang Zhang, Yueting Zhuang, Zhuowen Tu, Jingdong Wang. Deep Convolutional Neural Networks with Merge-and-Run Mappings.

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  • BibTeX @MISC{Pinheiro14weaklysupervised, author = {Pedro H. O. Pinheiro and Ronan Collobert and Pedro O. Pinheiro and Ronan Collobert}, title = {Weakly Supervised Object Segmentation with Convolutional Neural Networks}, year = {2014}}
  • Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation...

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Data Selection for training Semantic Segmentation CNNs with cross-dataset weak supervision. ... Network 10(1), January 2018. PDF BibTeX. ... and online trained fully ...

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Therefore, this study proposes a semantic segmentation method for remote sensing image on the basis of Deep Fusion Networks (DFN) combined with a conditional random field model.The method initially builds a DFN model in a Fully Convolutional Network (FCN) framework with a deconvolutional fusion structure.

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Fully convolutional networks can efficiently learn to make dense predictions for per-pixel tasks like semantic segmentation. mentation using convnets, and extensions to FCNs. The fol- lowing sections explain FCN design, introduce our architec- ture with in-network upsampling and skip layers, and de- scribe our experimental framework.

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Deep convolutional neural networks have become a key element in the recent breakthrough of salient object detection. However, existing CNN-based methods are based on either patch-wis (region-wise) training and inference or fully convolutional networks.

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Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, improve on the previous best result in semantic segmentation. .. Our key insight is to build "fully convolutional" networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.

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