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# Deep Learning for Neuroimage

## Deep Learning in general
* Textbook
  * Deep Learning Book (Yoshua Bengio) [[html]](http://www.deeplearningbook.org/) 
  
* Review papers
  * `2013` Representation learning: A review and new perspectives (Yushua Bengio) [[pdf]](http://www.cl.uni-heidelberg.de/courses/ws14/deepl/BengioETAL12.pdf)
  * `2014` Deep learning for neuroimaging: a validation study [[pdf]](http://journal.frontiersin.org/article/10.3389/fnins.2014.00229/full)
  * `2015 Nature` Deep learning (Yann LeCun, Yoshua Bengio, Geoffrey Hinton) [[pdf]](http://www.nature.com/nature/journal/v521/n7553/full/nature14539.html)
  * `2015` Deep learning in neural networks: An overview (J. Schmidhuber) [[pdf]](http://www2.econ.iastate.edu/tesfatsi/DeepLearningInNeuralNetworksOverview.JSchmidhuber2015.pdf)  
  * `2016` Understanding deep convolutional networks [[pdf]](http://rsta.royalsocietypublishing.org/content/374/2065/20150203)
  * `2016` Deep Learning in medical imaging: overview and future promise of an exciting new technique [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7463094)
  * `2016.07` Towards an integration of Deep Learning and Neuroscience [[pdf]](http://biorxiv.org/content/biorxiv/early/2016/06/13/058545.full.pdf)

* Network Models    
  * `2012` ImageNet classification with deep convolutional neural networks (A. Krizhevsky et al. Hinton) [[pdf]](http://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)
  * `2015` Fully convolutional networks for semantic segmentation (J. Long et al.) [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Long_Fully_Convolutional_Networks_2015_CVPR_paper.pdf)
  * `2014` Very deep convolutional networks for large-scale image recognition (K. Simonyan and A. Zisserman) [[pdf]](http://arxiv.org/pdf/1409.1556)
  * `2014` Visualizing and understanding convolutional networks (M. Zeiler and R. Fergus) [[pdf]](http://arxiv.org/pdf/1311.2901)
  * `2015` Fast R-CNN (R. Girshick) [[pdf]](http://www.cv-foundation.org/openaccess/content_iccv_2015/papers/Girshick_Fast_R-CNN_ICCV_2015_paper.pdf)
  * `2015` Going deeper with convolutions (C. Szegedy et al. Google) [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2015/papers/Szegedy_Going_Deeper_With_2015_CVPR_paper.pdf) 
  * `2016` Deep residual learning for image recognition (K. He et al. Microsoft) [[pdf]](http://arxiv.org/pdf/1512.03385)

* CNN
  * `2013` Decaf: A deep convolutional activation feature for generic visual recognition (J. Donahue et al.) [[pdf]](http://arxiv.org/pdf/1310.1531)
  * `2014` DeepFace: Closing the Gap to Human-Level Performance in Face Verification (Y. Taigman et al.) [[pdf]](http://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Taigman_DeepFace_Closing_the_2014_CVPR_paper.pdf)
  * `2015` Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks (S. Ren et al.) [[pdf]](http://papers.nips.cc/paper/5638-faster-r-cnn-towards-real-time-object-detection-with-region-proposal-networks.pdf)  
  * `2015` Imagenet large scale visual recognition challenge (O. Russakovsky et al.) [[pdf]](http://arxiv.org/pdf/1409.0575)  
  * `2015` [A Neural Algorithm of Artistic Style](http://sanghyukchun.github.io/92)
  
* CNN visuatlization
  * Visualizing and understanding convolutional networks [[pdf]](http://www.cs.nyu.edu/~fergus/papers/zeilerECCV2014.pdf)
  * mNeuron: a Matlab plugin to visualize neurons from deep models [[html]](http://vision03.csail.mit.edu/cnn_art/index.html)
  * Deep visualization toolbox [[code]](https://github.com/yosinski/deep-visualization-toolbox)
  * 3D visualization of a convolutional neural network [[demo]](http://scs.ryerson.ca/~aharley/vis/conv/)
  * Understanding neural networks through deep visualization [[html]](http://yosinski.com/deepvis)
  * Convolutional neural networks for visual recognition [[html]](http://cs231n.github.io/understanding-cnn/)
  * `2015` Deep Dream: visualizing every layer of GoogLeNet [[html]](http://www.pyimagesearch.com/2015/08/03/deep-dream-visualizing-every-layer-of-googlenet/)
  * `2016` Visualization of deep convolutional neural networks [[pdf]](http://openscholarship.wustl.edu/cgi/viewcontent.cgi?article=1150&context=eng_etds)  
  
* Articles
  * `2016.03` Deep learning in medical imaging: the not-so-near future [[html]](http://www.diagnosticimaging.com/pacs-and-informatics/deep-learning-medical-imaging-not-so-near-future)
  * `2016.04` Deep learning used to assist overburdened diagnosticians [[html]](https://www.sciencedaily.com/releases/2016/04/160404134050.htm)
  * `2016.08` AI startups in Healthcare [[html]](https://www.cbinsights.com/blog/artificial-intelligence-startups-healthcare/)
  * `2016.09` Machine Intelligence in Medical Imaging Conference – Report [[html]](http://n2value.com/blog/machine-intelligence-in-medical-imaging-conference-report)
  * `2016.09` The role of AI in Healthcare [[html]](https://www.linkedin.com/pulse/role-ai-healthcare-in-depth-guide-thomas-riisgaard-hansen)
  * `2016.09` DeepMind wants its healthcare AI to charge by results - but first it needs your data [[html]](https://techcrunch.com/2016/09/20/deepmind-wants-its-healthcare-ai-to-charge-by-results-but-first-it-needs-your-data/)
  * `2016.09` Microsoft announces new AI-powered health care initiatives targeting cancer [[html]]( http://www.theverge.com/2016/9/20/12986314/microsoft-ai-healthcare-project-hanover-cancer)
  * `2016.09` [Why deep learning is suddenly changing your life](http://fortune.com/ai-artificial-intelligence-deep-machine-learning/)
  
* Link 
  * [Awesome - Most Cited Deep Learning Papers](https://github.com/terryum/awesome-deep-learning-papers)
  * [Links to Deep Learning Subtopics](http://memkite.com/deep-learning-bibliography/)
  * [Book: Fundamental of Deep Learning](https://tensorflowkorea.wordpress.com/2016/04/18/fundamental-of-deep-learning-preview/)    
  * [머신러닝 튜토리얼](http://laonple.blog.me/220463627091)

* Topic
  * `2016.09` [인공지능이 작곡한 세계 최초의 음악이 공개되다](http://visla.kr/?p=45671)
  
  
## TensorFlow
* Tutorials
  * [TensorFlow 공식 홈페이지](http://tensorflow.org)
  * [TensorFlow Github](https://github.com/tensorflow/tensorflow/releases)
  * [TensorFlow Playground](http://playground.tensorflow.org/)
  * [Deep LEarning Docker Image](https://hub.docker.com/r/imcomking/bi_deeplearning/)
  * [텐서플로우 시작하기 (김정주)](https://gist.github.com/haje01/202ac276bace4b25dd3f)
  * [텐서플로우 코리아](https://tensorflowkorea.wordpress.com/)
  * [텐서플로우 튜토리얼 (텐서플로우 코리아)](https://tensorflowkorea.wordpress.com/2015/12/04/%ED%85%90%EC%84%9C%ED%94%8C%EB%A1%9C%EC%9A%B0-%ED%8A%9C%ED%86%A0%EB%A6%AC%EC%96%BC-1/comment-page-1/)
  * [Book: 텐서플로우 첫걸음](https://tensorflowkorea.wordpress.com/2015/12/04/%ED%85%90%EC%84%9C%ED%94%8C%EB%A1%9C%EC%9A%B0-%ED%8A%9C%ED%86%A0%EB%A6%AC%EC%96%BC-1/comment-page-1/)  
  * [Lecture: 모두를 위한 딥러닝/머신러닝 강의 TensorFlow (김성)](http://hunkim.github.io/ml/)
  * [Lecture: TensorFlow 로 시작하는 기계 학습과 딥 러닝 (CodeOnWeb)](https://codeonweb.com/course/7e8c4944-308e-410e-85aa-644624613741)
  * [TensorFlow Tutorial (SNU BILAB)](https://github.com/bi-lab/deeplearning_tutorial/blob/master/Deep_RL_tensorflow/TensorFlow_Tutorial.ipynb) 
  * [Deep learning/TensorFlow (문동선)](http://dsmoon.tistory.com/category/Deep%20Learning/TensorFlow)
  * [TFlearn](http://tflearn.org/)  
  
* Open-source TensorFlow Implementation
  * [Syntax Net, Magenta, Image2Txt](https://github.com/tensorflow/models)
  * [https://github.com/TensorFlowKR/awesome_tensorflow_implementations](https://github.com/TensorFlowKR/awesome_tensorflow_implementations)

## Deep learning for Neuroinformatics
  * `2015` Deep Neural Networks: a new fraomework for modeling biological vision and brain information processing [[pdf]](http://www.annualreviews.org/doi/pdf/10.1146/annurev-vision-082114-035447)
  
## Deep learning for neurological disorder
  * `2014` Deep learning for neuroimaging: a validation study [[pdf]](http://journal.frontiersin.org/article/10.3389/fnins.2014.00229/full)

## Deep learning for Segmentation
* Electron Microscopy  
  * `2013` Large-scale automatic reconstruction of neuroanl processes from electron microscopy images [[pdf]](https://arxiv.org/pdf/1303.7186.pdf)
  * `2016` Deep learning trends for focal brain pathology segmentation in MRI [[pdf]](https://arxiv.org/pdf/1607.05258.pdf)
* MRI
  * `2013` Machine learning in medical imaging [[pdf]](http://link.springer.com/chapter/10.1007%2F978-3-319-02267-3_1)
  * `D

## Deep learning for Brain Tumor Segmentation
* CODE
  * ISBI 2012 brain EM image segmentation [[github]](https://github.com/ahmed-fakhry/dive)
  * Efficient multi-scale 3D convolution neural network for brain lesion segmentation [[github]](https://github.com/Kamnitsask/deepmedic)
* MRI
  * `2012` A comparative study of MRI data using various Machine Learning and pattern recognition algorithms to Detect Brain Abnormalities [[pdf]](http://crpit.com/confpapers/CRPITV134Singh.pdf) = A novel machine learning approach for detecting the Brain Abnormalities from MRI structural images [[html]](http://link.springer.com/chapter/10.1007%2F978-3-642-34123-6_9#page-1)
  * `2014` Survey of intelligent methods for Brain Tumor Detection [[pdf]](http://www.ijcsi.org/papers/IJCSI-11-5-1-108-117.pdf)
  * `2015` Brain tumor detection and segmentation in multisequence MRI [[pdf]](https://www.vutbr.cz/www_base/zav_prace_soubor_verejne.php?file_id=109549)
  * `2015` Automated glioma segmentation in MRI using deep convolutional networks  [[pdf]](http://www.diva-portal.org/smash/get/diva2:841518/FULLTEXT01.pdf)
  * `2015` Learning with Difference of Gaussian Features in the 3D Segmentation of Glioblastoma Brain Tumors [[pdf]](http://cs229.stanford.edu/proj2015/277_report.pdf)
  * `2015` Multi-scale 3D convolutional neural networks for lesion segmentation in brain MRI [[pdf]](http://www.doc.ic.ac.uk/~bglocker/pdfs/kamnitsas2015isles.pdf)
  * `2015` MICCAI-BRATS 2015 proceedings [[pdf]](http://people.csail.mit.edu/menze/papers/proceedings_miccai_brats_2015.pdf)
    * Structured prediction with convolutional neural networks for multimodal brain tumor segmentation
    * A convolutional neural network approach to brain tumor segmentation
    * Multimodal brain tumor segmentation (BRATS) using Sparse Coding and 2-layer Neural Network
    * Deep convolutional neural networks for the segmentation of gliomas in multi-sequence MRI
    * Brain tumor segmentation with Deep Learning
    * Multi-modal brain tumor segmentation using Stacked Denoising Autoencoders
  * `2015` Brain tumor detection and classification using deep learning classifier on MRI images  [[html]](http://www.maxwellsci.com/jp/abstract.php?jid=RJASET&no=547&abs=08)
  * `2015` Detection and segmentation of brain metastases with deep convolutional networks [[pdf]](http://www.diva-portal.se/smash/get/diva2:853460/FULLTEXT01.pdf)
  * `2015` Deep Feature Learning with discrimination mechanism for brain tumor segmentation and diagnosis [[pdf]](http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7415818) 
  * `2015 Plos One` Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification [[pdf]](http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0125143) 
  * `2015 CVPR` Deep neural networks for anatomical brain segmentation [[pdf]](https://www.semanticscholar.org/paper/Deep-neural-networks-for-anatomical-brain-Br%C3%A9bisson-Montana/1689c752d566a2b3bdee46d0b87d7623c66218d0)
  * `2016` Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images [[pdf]](http://ieeexplore.ieee.org/document/7426413/?tp=&arnumber=7426413&punumber%3D42)
  * `2016 Stanford Report` A new algorithm for fully automatic brain tumor segmentation with 3D convolutional Neural Networks [[pdf]](http://cs231n.stanford.edu/reports2016/322_Report.pdf)
  * `2016` On image segmentation methods applied to glioblastoma: state of art and new trends [[pdf]](https://hal.archives-ouvertes.fr/hal-01325355/document)
  * `2016` Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation [[pdf]](http://arxiv.org/pdf/1603.05959v2.pdf)
* Pathology
  * `2012` Deep Neural Networks segment neuronal membranes in electron Microscopy images [[pdf]](http://papers.nips.cc/paper/4741-deep-neural-networks-segment-neuronal-membranes-in-electron-microscopy-images)
  
## Deep learning for Brain Tumor Grading
* MRI
  * `2015 IEEE EMBS` Brain tumor grading based on neural networks and convolutional neural netsworks [[pdf]](http://www.ncbi.nlm.nih.gov/pubmed/26736358)
  * `2016 Comput Math Methods Med` Multiscale CNNs for brain tumor segmentation and diagnosis [[pdf]](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4812495/pdf/CMMM2016-8356294.pdf)
* PET
  * `2016` Convolutional neural network can help differentiate FDG PET images of brain tumor between glioblastoma and primary central nervous system lymphoma [[html](http://jnm.snmjournals.org/content/57/supplement_2/1855?related-urls=yes&legid=jnumed;57/supplement_2/1855)
* Brain pathology images
  * `2015` Automated grading of gliomas using deep learning in digital pathology images: a modular approach with ensemble of convolutional neural networks [[pdf]](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4765616/pdf/2243353.pdf)
* Prostate/chest pathology images
  * `2016 Nature Sci.Rep.` Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis [[pdf]](http://www.nature.com/articles/srep26286)
  * `2015` Deep learning with non-medical training used for chest pathology identification [[pdf]](https://www.cs.tau.ac.il/~wolf/papers/SPIE15chest.pdf)

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