@inproceedings{He2016,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
booktitle = {Proceedings of the IEEE conference on computer vision and pattern recognition},
file = {:C$\backslash$:/Users/madam/OneDrive/Documents/Mendeley Desktop/He et al. - 2016 - Deep residual learning for image recognition(2).pdf:pdf},
mendeley-groups = {Deep{\_}Learning/Breakthrough,{\_}Manuscripts/MIDL{\_}2020{\_}LFNET,{\_}Manuscripts/MIDL{\_}2020{\_}LFNET/ResNet},
pages = {770--778},
title = {{Deep residual learning for image recognition}},
year = {2016}
}
@article{Jegou2017,
abstract = {State-of-the-art approaches for semantic image segmentation are built on Convolutional Neural Networks (CNNs). The typical segmentation architecture is composed of (a) a downsampling path responsible for extracting coarse semantic features, followed by (b) an upsampling path trained to recover the input image resolution at the output of the model and, optionally, (c) a post-processing module (e.g. Conditional Random Fields) to refine the model predictions. Recently, a new CNN architecture, Densely Connected Convolutional Networks (DenseNets), has shown excellent results on image classification tasks. The idea of DenseNets is based on the observation that if each layer is directly connected to every other layer in a feed-forward fashion then the network will be more accurate and easier to train. In this paper, we extend DenseNets to deal with the problem of semantic segmentation. We achieve state-of-the-art results on urban scene benchmark datasets such as CamVid and Gatech, without any further post-processing module nor pretraining. Moreover, due to smart construction of the model, our approach has much less parameters than currently published best entries for these datasets.},
archivePrefix = {arXiv},
arxivId = {1611.09326},
author = {Jegou, Simon and Drozdzal, Michal and Vazquez, David and Romero, Adriana and Bengio, Yoshua},
doi = {10.1109/CVPRW.2017.156},
eprint = {1611.09326},
file = {:C$\backslash$:/Users/madam/OneDrive/Documents/Mendeley Desktop/Jegou et al. - 2017 - The One Hundred Layers Tiramisu Fully Convolutional DenseNets for Semantic Segmentation.pdf:pdf},
isbn = {9781538607336},
issn = {21607516},
journal = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
mendeley-groups = {{\_}Manuscripts/MIDL{\_}2020{\_}LFNET},
pages = {1175--1183},
title = {{The One Hundred Layers Tiramisu: Fully Convolutional DenseNets for Semantic Segmentation}},
volume = {2017-July},
year = {2017}
}

@article{Huang2017,
abstract = {Recent work has shown that convolutional networks can be substantially deeper, more accurate, and efficient to train if they contain shorter connections between layers close to the input and those close to the output. In this paper, we embrace this observation and introduce the Dense Convolutional Network (DenseNet), which connects each layer to every other layer in a feed-forward fashion. Whereas traditional convolutional networks with L layers have L connections - one between each layer and its subsequent layer - our network has L(L2+1) direct connections. For each layer, the feature-maps of all preceding layers are used as inputs, and its own feature-maps are used as inputs into all subsequent layers. DenseNets have several compelling advantages: they alleviate the vanishing-gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters. We evaluate our proposed architecture on four highly competitive object recognition benchmark tasks (CIFAR-10, CIFAR-100, SVHN, and ImageNet). DenseNets obtain significant improvements over the state-of-the-art on most of them, whilst requiring less computation to achieve high performance. Code and pre-trained models are available at https://github.com/liuzhuang13/DenseNet.},
archivePrefix = {arXiv},
arxivId = {1608.06993},
author = {Huang, Gao and Liu, Zhuang and {Van Der Maaten}, Laurens and Weinberger, Kilian Q.},
doi = {10.1109/CVPR.2017.243},
eprint = {1608.06993},
file = {:C$\backslash$:/Users/madam/OneDrive/Documents/Mendeley Desktop/Huang et al. - 2017 - Densely connected convolutional networks.pdf:pdf},
isbn = {9781538604571},
journal = {Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017},
mendeley-groups = {{\_}Manuscripts/MIDL{\_}2020{\_}LFNET},
pages = {2261--2269},
title = {{Densely connected convolutional networks}},
volume = {2017-January},
year = {2017}
}


@article{Ke2019,
author = {Ke, Rihuan and Bugeau, Aur{\'{e}}lie and Papadakis, Nicolas and Schuetz, Peter and Ke, Rihuan and Bugeau, Aur{\'{e}}lie and Papadakis, Nicolas and Schuetz, Peter and A, Carola-bibiane Sch{\"{o}}nlieb and Ke, Rihuan and Papadakis, Nicolas and Schuetz, Peter},
file = {:C$\backslash$:/Users/madam/OneDrive/Documents/Mendeley Desktop/Ke et al. - 2019 - A multi-task U-net for segmentation with lazy labels To cite this version HAL Id hal-02170180 A multi-task U-net fo.pdf:pdf},
mendeley-groups = {{\_}to{\_}read/2019-2020/Psudo-labelling},
title = {{A multi-task U-net for segmentation with lazy labels To cite this version : HAL Id : hal-02170180 A multi-task U-net for segmentation with lazy labels}},
year = {2019}
}

@article{Venhuizen2018,
abstract = {We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.},
author = {Venhuizen, Freerk G. and van Ginneken, Bram and Liefers, Bart and van Asten, Freekje and Schreur, Vivian and Fauser, Sascha and Hoyng, Carel and Theelen, Thomas and S{\'{a}}nchez, Clara I.},
doi = {10.1364/boe.9.001545},
file = {:C$\backslash$:/Users/madam/OneDrive/Documents/Mendeley Desktop/Venhuizen et al. - 2018 - Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optic.pdf:pdf},
issn = {2156-7085},
journal = {Biomedical Optics Express},
mendeley-groups = {{\_}Manuscripts/Retinal{\_}Layer{\_}Fluid{\_}Segmentation},
number = {4},
pages = {1545},
title = {{Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography}},
volume = {9},
year = {2018}
}

@article{rathke2014probabilistic,
  title={{Probabilistic intra-retinal layer segmentation in 3-D OCT images using global shape regularization}},
  author={Rathke, Fabian and Schmidt, Stefan and Schn{\"o}rr, Christoph},
  journal={Medical image analysis},
  volume={18},
  number={5},
  pages={781--794},
  year={2014},
  publisher={Elsevier}
}

@article{koozekanani2001retinal,
  title={{Retinal thickness measurements from optical coherence tomography using a Markov boundary model}},
  author={Koozekanani, Dara and Boyer, Kim and Roberts, Cynthia},
  journal={IEEE transactions on medical imaging},
  volume={20},
  number={9},
  pages={900--916},
  year={2001},
  publisher={IEEE}
}

@article{carass2014multiple,
  title={{Multiple-object geometric deformable model for segmentation of macular OCT}},
  author={Carass, Aaron and Lang, Andrew and Hauser, Matthew and Calabresi, Peter A and Ying, Howard S and Prince, Jerry L},
  journal={Biomedical optics express},
  volume={5},
  number={4},
  pages={1062--1074},
  year={2014},
  publisher={Optical Society of America}
}

@article{oliveira2017multi,
  title={{Multi-surface segmentation of OCT images with AMD using sparse high order potentials}},
  author={Oliveira, Jorge and Pereira, S{\'e}rgio and Gon{\c{c}}alves, Lu{\'\i}s and Ferreira, Manuel and Silva, Carlos A},
  journal={Biomedical optics express},
  volume={8},
  number={1},
  pages={281--297},
  year={2017},
  publisher={Optical Society of America}
}

@article{chiu2010automatic,
  title={{Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation}},
  author={Chiu, Stephanie J and Li, Xiao T and Nicholas, Peter and Toth, Cynthia A and Izatt, Joseph A and Farsiu, Sina},
  journal={Optics express},
  volume={18},
  number={18},
  pages={19413--19428},
  year={2010},
  publisher={Optical Society of America}
}

@article{chen2012three,
  title={{Three-dimensional segmentation of fluid-associated abnormalities in retinal OCT: probability constrained graph-search-graph-cut}},
  author={Chen, Xinjian and Niemeijer, Meindert and Zhang, Li and Lee, Kyungmoo and Abr{\`a}moff, Michael D and Sonka, Milan},
  journal={IEEE transactions on medical imaging},
  volume={31},
  number={8},
  pages={1521--1531},
  year={2012},
  publisher={IEEE}
}

@article{lee2013comparative,
author = {Lee, Sieun and Fallah, Nader and Forooghian, Farzin and Ko, Ashley and Pakzad-Vaezi, Kaivon and Merkur, Andrew B. and Kirker, Andrew W. and Albiani, David A. and Young, Mei and Sarunic, Marinko V. and Beg, Mirza Faisal},
title = {{Comparative Analysis of Repeatability of Manual and Automated Choroidal Thickness Measurements in Nonneovascular Age-Related Macular Degeneration}},
journal = {Investigative Ophthalmology \& Visual Science},
volume = {54},
number = {4},
pages = {2864},
year = {2013},
}

@article{lang2013retinal,
  title={{Retinal layer segmentation of macular OCT images using boundary classification}},
  author={Lang, Andrew and Carass, Aaron and Hauser, Matthew and Sotirchos, Elias S and Calabresi, Peter A and Ying, Howard S and Prince, Jerry L},
  journal={Biomedical optics express},
  volume={4},
  number={7},
  pages={1133--1152},
  year={2013},
  publisher={Optical Society of America}
}

@article{srinivasan2014automatic,
  title={{Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology}},
  author={Srinivasan, Pratul P and Heflin, Stephanie J and Izatt, Joseph A and Arshavsky, Vadim Y and Farsiu, Sina},
  journal={Biomedical optics express},
  volume={5},
  number={2},
  pages={348--365},
  year={2014},
  publisher={Optical Society of America}
}

@article{fang2017automatic,
  title={{Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search}},
  author={Fang, Leyuan and Cunefare, David and Wang, Chong and Guymer, Robyn H and Li, Shutao and Farsiu, Sina},
  journal={Biomedical Optics Express},
  volume={8},
  number={5},
  pages={2732--2744},
  year={2017},
  publisher={Optical Society of America}
}

@inproceedings{long2015fully,
  title={{Fully convolutional networks for semantic segmentation}},
  author={Long, Jonathan and Shelhamer, Evan and Darrell, Trevor},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3431--3440},
  year={2015}
}

@incollection{he2017towards,
  title={{Towards Topological Correct Segmentation of Macular OCT from Cascaded FCNs}},
  author={He, Yufan and Carass, Aaron and Yun, Yeyi and Zhao, Can and Jedynak, Bruno M and Solomon, Sharon D and Saidha, Shiv and Calabresi, Peter A and Prince, Jerry L},
  booktitle={Fetal, Infant and Ophthalmic Medical Image Analysis},
  pages={202--209},
  year={2017},
  publisher={Springer}
}

@inproceedings{bentaieb2016topology,
  title={Topology aware fully convolutional networks for histology gland segmentation},
  author={BenTaieb, A{\"\i}cha and Hamarneh, Ghassan},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={460--468},
  year={2016},
  organization={Springer}
}

@article{roy2017relaynet,
  title={{ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks}},
  author={Roy, Abhijit Guha and Conjeti, Sailesh and Karri, Sri Phani Krishna and Sheet, Debdoot and Katouzian, Amin and Wachinger, Christian and Navab, Nassir},
  journal={Biomedical optics express},
  volume={8},
  number={8},
  pages={3627--3642},
  year={2017},
  publisher={Optical Society of America}
}

@article{he2018topology,
  title={{Topology guaranteed segmentation of the human retina from OCT using convolutional neural networks}},
  author={He, Yufan and Carass, Aaron and Jedynak, Bruno M and Solomon, Sharon D and Saidha, Shiv and Calabresi, Peter A and Prince, Jerry L},
  journal={arXiv preprint arXiv:1803.05120},
  year={2018}
}

@inproceedings{ronneberger2015u,
  title={U-net: Convolutional networks for biomedical image segmentation},
  author={Ronneberger, Olaf and Fischer, Philipp and Brox, Thomas},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={234--241},
  year={2015},
  organization={Springer}
}

@article{srivastava2014dropout,
  title={Dropout: a simple way to prevent neural networks from overfitting.},
  author={Srivastava, Nitish and Hinton, Geoffrey E and Krizhevsky, Alex and Sutskever, Ilya and Salakhutdinov, Ruslan},
  journal={Journal of Machine Learning Research},
  volume={15},
  number={1},
  pages={1929--1958},
  year={2014}
}

@inproceedings{glorot2010understanding,
  title={Understanding the difficulty of training deep feedforward neural networks},
  author={Glorot, Xavier and Bengio, Yoshua},
  booktitle={Proceedings of the thirteenth international conference on artificial intelligence and statistics},
  pages={249--256},
  year={2010}
}

@article{goldstein2010geometric,
  title={{Geometric applications of the split Bregman method: segmentation and surface reconstruction}},
  author={Goldstein, Tom and Bresson, Xavier and Osher, Stanley},
  journal={Journal of Scientific Computing},
  volume={45},
  number={1-3},
  pages={272--293},
  year={2010},
  publisher={Springer}
}

@article{breiman2001random,
  title={Random forests},
  author={Breiman, Leo},
  journal={Machine learning},
  volume={45},
  number={1},
  pages={5--32},
  year={2001},
  publisher={Springer}
}

@article{lu2019deep,
  title={Deep-Learning based Multiclass Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using a Fully Convolutional Neural Network},
  author={Lu, Donghuan and Heisler, Morgan and Lee, Sieun and Ding, Gavin Weiguang and Navajas, Eduardo and Sarunic, Marinko V and Beg, Mirza Faisal},
  journal={Medical Image Analysis},
  year={2019},
  volume={54},
  pages={100-110},
  publisher={Elsevier}
}

@article{yu2015multi,
  title={Multi-scale context aggregation by dilated convolutions},
  author={Yu, Fisher and Koltun, Vladlen},
  journal={arXiv preprint arXiv:1511.07122},
  year={2015}
}

@misc{tensorflow2015-whitepaper,
title={{TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems}},
url={http://tensorflow.org/},
note={Software available from tensorflow.org},
author={
    Mart\'{\i}n~Abadi and
    Ashish~Agarwal and
    Paul~Barham and 
    et al.},
  year={2015},
}

@article{fujimoto1995,
  title={Optical coherence tomography of the human retina},
  author={Hee, MR and Izatt, JA and Swanson, EA and Huang, D and Schuman, JS and Lin, CP and Puliafito, CA and Fujimoto, JG},
  journal={Arch Ophthalmol.},
  volume={113},
  number={3},
  pages={325--332},
  year={1995},
  publisher={Archives of ophthalmology}
}

@book{joussen2010retinal,
  title={{Retinal Vascular Disease}},
  author={Joussen, A.M. and Gardner, T.W. and Kirchhof, B. and Ryan, S.J.},
  isbn={9783540295419},
  lccn={2007931904},
  year={2010},
  publisher={Springer Berlin Heidelberg}
}

@article{weng2016decreasing,
  title={Decreasing incidence of type 2 diabetes mellitus in the United States, 2007--2012: epidemiologic findings from a large US claims database},
  author={Weng, Wayne and Liang, Yuanjie and Kimball, Edward S and Hobbs, Todd and Kong, Sheldon X and Sakurada, Brian and Bouchard, Jonathan},
  journal={Diabetes research and clinical practice},
  volume={117},
  pages={111--118},
  year={2016},
  publisher={Elsevier}
}