% #####################################################################
% introduction ########################################################
% #####################################################################

% Classifcation tissue
@article{klein2019maldi,
  title={MALDI-Imaging for Classification of Epithelial Ovarian Cancer Histotypes from a Tissue Microarray Using Machine Learning Methods},
  author={Klein, Oliver and Kanter, Frederic and Kulbe, Hagen and Jank, Paul and Denkert, Carsten and Nebrich, Grit and Schmitt, Wolfgang D and Wu, Zhiyang and Kunze, Catarina A and Sehouli, Jalid and others},
  journal={PROTEOMICS--Clinical Applications},
  volume={13},
  number={1},
  pages={1700181},
  year={2019},
  publisher={Wiley Online Library}
}

@inproceedings{tzeng2017adversarial,
  title={Adversarial discriminative domain adaptation},
  author={Tzeng, Eric and Hoffman, Judy and Saenko, Kate and Darrell, Trevor},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={7167--7176},
  year={2017}
}

@inproceedings{ganin2015unsupervised,
  title={Unsupervised domain adaptation by backpropagation},
  author={Ganin, Yaroslav and Lempitsky, Victor},
  booktitle={International conference on machine learning},
  pages={1180--1189},
  year={2015}
}

@inproceedings{chen2019synergistic,
  title={Synergistic image and feature adaptation: Towards cross-modality domain adaptation for medical image segmentation},
  author={Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng-Ann},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={33},
  pages={865--872},
  year={2019}
}

@inproceedings{kim2019diversify,
  title={Diversify and match: A domain adaptive representation learning paradigm for object detection},
  author={Kim, Taekyung and Jeong, Minki and Kim, Seunghyeon and Choi, Seokeon and Kim, Changick},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={12456--12465},
  year={2019}
}

@inproceedings{bousmalis2017unsupervised,
  title={Unsupervised pixel-level domain adaptation with generative adversarial networks},
  author={Bousmalis, Konstantinos and Silberman, Nathan and Dohan, David and Erhan, Dumitru and Krishnan, Dilip},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={3722--3731},
  year={2017}
}

@article{ge2020self,
  title={Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID},
  author={Ge, Yixiao and Chen, Dapeng and Zhu, Feng and Zhao, Rui and Li, Hongsheng},
  journal={arXiv preprint arXiv:2006.02713},
  year={2020}
}


@inproceedings{wu2018unsupervised,
  title={Unsupervised feature learning via non-parametric instance discrimination},
  author={Wu, Zhirong and Xiong, Yuanjun and Yu, Stella X and Lin, Dahua},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3733--3742},
  year={2018}
}

@article{chen2020unsupervised,
  title={Unsupervised bidirectional cross-modality adaptation via deeply synergistic image and feature alignment for medical image segmentation},
  author={Chen, Cheng and Dou, Qi and Chen, Hao and Qin, Jing and Heng, Pheng Ann},
  journal={IEEE Transactions on Medical Imaging},
  year={2020},
  publisher={IEEE}
}

@inproceedings{saito2018maximum,
  title={Maximum classifier discrepancy for unsupervised domain adaptation},
  author={Saito, Kuniaki and Watanabe, Kohei and Ushiku, Yoshitaka and Harada, Tatsuya},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={3723--3732},
  year={2018}
}





@article{tam2016method,
  title={A method for normalizing pathology images to improve feature extraction for quantitative pathology},
  author={Tam, Allison and Barker, Jocelyn and Rubin, Daniel},
  journal={Medical physics},
  volume={43},
  number={1},
  pages={528--537},
  year={2016},
  publisher={Wiley Online Library}
}


@article{zarella2017alternative,
  title={An alternative reference space for H\&E color normalization},
  author={Zarella, Mark D and Yeoh, Chan and Breen, David E and Garcia, Fernando U},
  journal={PloS one},
  volume={12},
  number={3},
  pages={e0174489},
  year={2017},
  publisher={Public Library of Science San Francisco, CA USA}
}

@inproceedings{CycleGAN2017,
  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
  booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
  year={2017}
}

@inproceedings{gadermayr2018way,
  title={Which way round? a study on the performance of stain-translation for segmenting arbitrarily dyed histological images},
  author={Gadermayr, Michael and Appel, Vitus and Klinkhammer, Barbara M and Boor, Peter and Merhof, Dorit},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={165--173},
  year={2018},
  organization={Springer}
}

% Weakly sup - tumor vs non tumor
@article{campanella2019clinical,
  title={Clinical-grade computational pathology using weakly supervised deep learning on whole slide images},
  author={Campanella, Gabriele and Hanna, Matthew G and Geneslaw, Luke and Miraflor, Allen and Silva, Vitor Werneck Krauss and Busam, Klaus J and Brogi, Edi and Reuter, Victor E and Klimstra, David S and Fuchs, Thomas J},
  journal={Nature medicine},
  volume={25},
  number={8},
  pages={1301--1309},
  year={2019},
  publisher={Nature Publishing Group}
}

@inproceedings{chan2019histosegnet,
  title={Histosegnet: Semantic segmentation of histological tissue type in whole slide images},
  author={Chan, Lyndon and Hosseini, Mahdi S and Rowsell, Corwyn and Plataniotis, Konstantinos N and Damaskinos, Savvas},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={10662--10671},
  year={2019}
}

@article{mobadersany2018predicting,
  title={Predicting cancer outcomes from histology and genomics using convolutional networks},
  author={Mobadersany, Pooya and Yousefi, Safoora and Amgad, Mohamed and Gutman, David A and Barnholtz-Sloan, Jill S and Vega, Jos{\'e} E Vel{\'a}zquez and Brat, Daniel J and Cooper, Lee AD},
  journal={Proceedings of the National Academy of Sciences},
  volume={115},
  number={13},
  pages={E2970--E2979},
  year={2018},
  publisher={National Acad Sciences}
}

@inproceedings{couture2018multiple,
  title={Multiple instance learning for heterogeneous images: Training a cnn for histopathology},
  author={Couture, Heather D and Marron, James Stephen and Perou, Charles M and Troester, Melissa A and Niethammer, Marc},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={254--262},
  year={2018},
  organization={Springer}
}

% self-supervised learning
@inproceedings{abbet2020divide,
  title={Divide-and-Rule: Self-Supervised Learning for Survival Analysis in Colorectal Cancer},
  author={Abbet, Christian and Zlobec, Inti and Bozorgtabar, Behzad and Thiran, Jean-Philippe},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={480--489},
  year={2020},
  organization={Springer}
}


% Definition
@misc{abbeel2020unsup,
  author        = {Pieter, Abbeel anf Xi, Chen and Jonathan, Ho and Aravind, Srinivas and Alex, Li and Wilson, Yan},
  title         = {CS 294-158. Deep Unsupervised Learning},
  month         = {February},
  year          = {2020},
  publisher={UC Berkeley Electrical Enineering and Computer Science}
}

% autoencoders stacked - denoising
@article{vincent2010stacked,
  title={Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion},
  author={Vincent, Pascal and Larochelle, Hugo and Lajoie, Isabelle and Bengio, Yoshua and Manzagol, Pierre-Antoine},
  journal={Journal of machine learning research},
  volume={11},
  number={Dec},
  pages={3371--3408},
  year={2010}
}

%missing pece of image
@inproceedings{pathak2016context,
  title={Context encoders: Feature learning by inpainting},
  author={Pathak, Deepak and Krahenbuhl, Philipp and Donahue, Jeff and Darrell, Trevor and Efros, Alexei A},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={2536--2544},
  year={2016}
}

% PSlit brain l to ab , ab to l
@inproceedings{zhang2017split,
  title={Split-brain autoencoders: Unsupervised learning by cross-channel prediction},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={1058--1067},
  year={2017}
}

% COntext prediction north, east, ...
@inproceedings{doersch2015unsupervised,
  title={Unsupervised visual representation learning by context prediction},
  author={Doersch, Carl and Gupta, Abhinav and Efros, Alexei A},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={1422--1430},
  year={2015}
}

%jigsaw puzzle
@inproceedings{noroozi2016unsupervised,
  title={Unsupervised learning of visual representations by solving jigsaw puzzles},
  author={Noroozi, Mehdi and Favaro, Paolo},
  booktitle={European Conference on Computer Vision},
  pages={69--84},
  year={2016},
  organization={Springer}
}

% SimCLR
@article{chen2020simple,
  title={A simple framework for contrastive learning of visual representations},
  author={Chen, Ting and Kornblith, Simon and Norouzi, Mohammad and Hinton, Geoffrey},
  journal={arXiv preprint arXiv:2002.05709},
  year={2020}
}

% CPC - NCE (negative cross entropy) Loss and Mutual Information Estimation
@article{oord2018representation,
  title={Representation learning with contrastive predictive coding},
  author={Oord, Aaron van den and Li, Yazhe and Vinyals, Oriol},
  journal={arXiv preprint arXiv:1807.03748},
  year={2018}
}

% Supervised contrastive learning
@article{khosla2020supervised,
  title={Supervised Contrastive Learning},
  author={Khosla, Prannay and Teterwak, Piotr and Wang, Chen and Sarna, Aaron and Tian, Yonglong and Isola, Phillip and Maschinot, Aaron and Liu, Ce and Krishnan, Dilip},
  journal={arXiv preprint arXiv:2004.11362},
  year={2020}
}


% Cross-batch memory (XBM) to et negatives
@article{wang2019cross,
  title={Cross-Batch Memory for Embedding Learning},
  author={Wang, Xun and Zhang, Haozhi and Huang, Weilin and Scott, Matthew R},
  journal={arXiv preprint arXiv:1912.06798},
  year={2019}
}


% Anchors for embeeding and unsupervised approach - ANS !!!!
@article{huang2019unsupervised,
  title={Unsupervised deep learning by neighbourhood discovery},
  author={Huang, Jiabo and Dong, Qi and Gong, Shaogang and Zhu, Xiatian},
  journal={arXiv preprint arXiv:1904.11567},
  year={2019}
}

% Segmentation based on embedding ? Suppervised - kl divergence
@article{qaiser2019fast,
  title={Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features},
  author={Qaiser, Talha and Tsang, Yee-Wah and Taniyama, Daiki and Sakamoto, Naoya and Nakane, Kazuaki and Epstein, David and Rajpoot, Nasir},
  journal={Medical image analysis},
  volume={55},
  pages={1--14},
  year={2019},
  publisher={Elsevier}
}

@article{tellez2018gigapixel,
  title={Gigapixel Whole-Slide Image Classification Using Unsupervised Image Compression And Contrastive Training},
  author={Tellez, David and van der Laak, Jeroen and Ciompi, Francesco},
  journal={Medical Imaging with Deep Learning},
  year={2018}
}

% Margin loss
@inproceedings{schroff2015facenet,
  title={Facenet: A unified embedding for face recognition and clustering},
  author={Schroff, Florian and Kalenichenko, Dmitry and Philbin, James},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={815--823},
  year={2015}
}

% Self-sup. with contrastive loss with siamese network (shared) + need to know what is similar and dissimilar (based on spatial simi distance)
@article{gildenblat2019self,
  title={Self-supervised similarity learning for digital pathology},
  author={Gildenblat, Jacob and Klaiman, Eldad},
  journal= {2nd COMPAY Workshop at MICCAI 2019},
  year={2019}
}



% Logistic loss for contrastive
@article{mikolov2013efficient,
  title={Efficient estimation of word representations in vector space},
  author={Mikolov, Tomas and Chen, Kai and Corrado, Greg and Dean, Jeffrey},
  journal={arXiv preprint arXiv:1301.3781},
  year={2013}
}

% self-supervised learning
% Pseudo labels
@inproceedings{caron2018deep,
  title={Deep clustering for unsupervised learning of visual features},
  author={Caron, Mathilde and Bojanowski, Piotr and Joulin, Armand and Douze, Matthijs},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={132--149},
  year={2018}
}

% Normalization of colors
@inproceedings{anand2019fast,
  title={Fast GPU-Enabled Color Normalization for Digital Pathology},
  author={Anand, Deepak and Ramakrishnan, Goutham and Sethi, Amit},
  booktitle={2019 International Conference on Systems, Signals and Image Processing (IWSSIP)},
  pages={219--224},
  year={2019},
  organization={IEEE}
}

@inproceedings{zanjani2018deep,
  title={Deep Convolutional Gaussian Mixture Model for Stain-Color Normalization of Histopathological Images},
  author={Zanjani, Farhad Ghazvinian and Zinger, Svitlana and others},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={274--282},
  year={2018},
  organization={Springer}
}

@inproceedings{vahadane2015structure,
  title={Structure-preserved color normalization for histological images},
  author={Vahadane, Abhishek and Peng, Tingying and Albarqouni, Shadi and Baust, Maximilian and Steiger, Katja and Schlitter, Anna Melissa and Sethi, Amit and Esposito, Irene and Navab, Nassir},
  booktitle={2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)},
  pages={1012--1015},
  year={2015},
  organization={IEEE}
}

% Data augmentation pathology - 
@article{tellez2019quantifying,
  title={Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology},
  author={Tellez, David and Litjens, Geert and B{\'a}ndi, P{\'e}ter and Bulten, Wouter and Bokhorst, John-Melle and Ciompi, Francesco and van der Laak, Jeroen},
  journal={Medical image analysis},
  volume={58},
  pages={101544},
  year={2019},
  publisher={Elsevier}
}

% Rotational inv - sterabe filters
@article{ghosh2019scale,
  title={Scale steerable filters for locally scale-invariant convolutional neural networks},
  author={Ghosh, Rohan and Gupta, Anupam K},
  journal={arXiv preprint arXiv:1906.03861},
  year={2019}
}

% spatial correlation filters
@inproceedings{ye2019breast,
  title={Breast Cancer Image Classification on WSI with Spatial Correlations},
  author={Ye, Jiandong and Luo, Yihao and Zhu, Chuang and Liu, Fang and Zhang, Yue},
  booktitle={ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  pages={1219--1223},
  year={2019},
  organization={IEEE}
}

% Whole slide classification, multi scale - how to aggreate  
@article{vu2019methods,
  title={Methods for segmentation and classification of digital microscopy tissue images},
  author={Vu, Quoc Dang and Graham, Simon and Kurc, Tahsin and To, Minh Nguyen Nhat and Shaban, Muhammad and Qaiser, Talha and Koohbanani, Navid Alemi and Khurram, Syed Ali and Kalpathy-Cramer, Jayashree and Zhao, Tianhao and others},
  journal={Frontiers in bioengineering and biotechnology},
  volume={7},
  year={2019},
  publisher={Frontiers Media SA}
}

% Whole slide classification, multi scale - how to aggreate  
@inproceedings{sirinukunwattana2018improving,
  title={Improving whole slide segmentation through visual context-a systematic study},
  author={Sirinukunwattana, Korsuk and Alham, Nasullah Khalid and Verrill, Clare and Rittscher, Jens},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={192--200},
  year={2018},
  organization={Springer}
}

@InProceedings{pmlr-v102-bokhorst19a,
  title = 	 {Learning from sparsely annotated data for semantic segmentation in histopathology images},
  author = 	 {Bokhorst, {John-Melle} and Pinckaers, Hans and {van Zwam}, Peter and Nagtegaal, Iris and {van der Laak}, Jeroen and Ciompi, Francesco},
  booktitle = 	 {Proceedings of The 2nd International Conference on Medical Imaging with Deep Learning},
  pages = 	 {84--91},
  year = 	 {2019},
  volume = 	 {102},
  month = 	 {08--10 Jul},
  publisher = 	 {PMLR},
}

@article{wang2018deep,
  title={Deep visual domain adaptation: A survey},
  author={Wang, Mei and Deng, Weihong},
  journal={Neurocomputing},
  volume={312},
  pages={135--153},
  year={2018},
  publisher={Elsevier}
}

% Artifacts
@article{komura2018machine,
  title={Machine learning methods for histopathological image analysis},
  author={Komura, Daisuke and Ishikawa, Shumpei},
  journal={Computational and structural biotechnology journal},
  volume={16},
  pages={34--42},
  year={2018},
  publisher={Elsevier}
}

% Whole slide classification, multi scale - how to aggreate  
@article{gecer2018detection,
  title={Detection and classification of cancer in whole slide breast histopathology images using deep convolutional networks},
  author={Gecer, Baris and Aksoy, Selim and Mercan, Ezgi and Shapiro, Linda G and Weaver, Donald L and Elmore, Joann G},
  journal={Pattern recognition},
  volume={84},
  pages={345--356},
  year={2018},
  publisher={Elsevier}
}

% Compression of images using multitask learning "unsupervised ...humhum"
@article{tellez2020extending,
  title={Extending Unsupervised Neural Image Compression With Supervised Multitask Learning},
  author={Tellez, David and Hoppener, Diederik and Verhoef, Cornelis and Grunhagen, Dirk and Nierop, Pieter and Drozdzal, Michal and van der Laak, Jeroen and Ciompi, Francesco},
  journal={arXiv preprint arXiv:2004.07041},
  year={2020}
}

% Graph stufff - Yeah surroundings
@inproceedings{zhou2019cgc,
  title={Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images},
  author={Zhou, Yanning and Graham, Simon and Alemi Koohbanani, Navid and Shaban, Muhammad and Heng, Pheng-Ann and Rajpoot, Nasir},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision Workshops},
  pages={0--0},
  year={2019}
}

% Nuclei segmentation - (take surrounding into account)
@inproceedings{zhou2019cia,
  title={Cia-net: Robust nuclei instance segmentation with contour-aware information aggregation},
  author={Zhou, Yanning and Onder, Omer Fahri and Dou, Qi and Tsougenis, Efstratios and Chen, Hao and Heng, Pheng-Ann},
  booktitle={International Conference on Information Processing in Medical Imaging},
  pages={682--693},
  year={2019},
  organization={Springer}
}

% #####################################################################
% methods #############################################################
% #####################################################################

@inproceedings{he2020momentum,
  title={Momentum contrast for unsupervised visual representation learning},
  author={He, Kaiming and Fan, Haoqi and Wu, Yuxin and Xie, Saining and Girshick, Ross},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={9729--9738},
  year={2020}
}


@article{chen2020improved,
  title={Improved baselines with momentum contrastive learning},
  author={Chen, Xinlei and Fan, Haoqi and Girshick, Ross and He, Kaiming},
  journal={arXiv preprint arXiv:2003.04297},
  year={2020}
}

@article{kim2020cross,
  title={Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels},
  author={Kim, Donghyun and Saito, Kuniaki and Oh, Tae-Hyun and Plummer, Bryan A and Sclaroff, Stan and Saenko, Kate},
  journal={arXiv preprint arXiv:2003.08264},
  year={2020}
}

@inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}

% ML in Histology 
% Bible - Pathology + histology
@book{loda2016pathology,
  title={Pathology and epidemiology of cancer},
  author={Loda, Massimo and Mucci, Lorelei A and Mittelstadt, Megan L and Van Hemelrijck, Mieke and Cotter, Maura Br{\'\i}d},
  year={2016},
  publisher={Springer}
}

% TMA
@article{turkki2019breast,
  title={Breast cancer outcome prediction with tumour tissue images and machine learning},
  author={Turkki, Riku and Byckhov, Dmitrii and Lundin, Mikael and Isola, Jorma and Nordling, Stig and Kovanen, Panu E and Verrill, Clare and von Smitten, Karl and Joensuu, Heikki and Lundin, Johan and others},
  journal={Breast cancer research and treatment},
  volume={177},
  number={1},
  pages={41--52},
  year={2019},
  publisher={Springer}
}

% TMA bis
@article{geessink2019computer,
  title={Computer aided quantification of intratumoral stroma yields an independent prognosticator in rectal cancer},
  author={Geessink, Oscar GF and Baidoshvili, Alexi and Klaase, Joost M and Bejnordi, Babak Ehteshami and Litjens, Geert JS and van Pelt, Gabi W and Mesker, Wilma E and Nagtegaal, Iris D and Ciompi, Francesco and van der Laak, Jeroen AWM},
  journal={Cellular Oncology},
  volume={42},
  number={3},
  pages={331--341},
  year={2019},
  publisher={Springer}
}

% TMA - with survival output comparison as a second step
@article{arvaniti2018automated,
  title={Automated Gleason grading of prostate cancer tissue microarrays via deep learning},
  author={Arvaniti, Eirini and Fricker, Kim S and Moret, Michael and Rupp, Niels and Hermanns, Thomas and Fankhauser, Christian and Wey, Norbert and Wild, Peter J and Rueschoff, Jan H and Claassen, Manfred},
  journal={Scientific reports},
  volume={8},
  number={1},
  pages={1--11},
  year={2018},
  publisher={Nature Publishing Group}
}



% #####################################################################
% datasets #############################################################
% #####################################################################


% CAMELYON dataset (! breast)
@article{litjens20181399,
  title={1399 H\&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset},
  author={Litjens, Geert and Bandi, Peter and Ehteshami Bejnordi, Babak and Geessink, Oscar and Balkenhol, Maschenka and Bult, Peter and Halilovic, Altuna and Hermsen, Meyke and van de Loo, Rob and Vogels, Rob and others},
  journal={GigaScience},
  volume={7},
  number={6},
  pages={giy065},
  year={2018},
  publisher={Oxford University Press}
}

%  TUPAC Dataset challenge - lot of labels
@article{veta2019predicting,
  title={Predicting breast tumor proliferation from whole-slide images: the TUPAC16 challenge},
  author={Veta, Mitko and Heng, Yujing J and Stathonikos, Nikolas and Bejnordi, Babak Ehteshami and Beca, Francisco and Wollmann, Thomas and Rohr, Karl and Shah, Manan A and Wang, Dayong and Rousson, Mikael and others},
  journal={Medical image analysis},
  volume={54},
  pages={111--121},
  year={2019},
  publisher={Elsevier}
}

% From TCGA
@article{weinstein2013cancer,
  title={The cancer genome atlas pan-cancer analysis project},
  author={Weinstein, John N and Collisson, Eric A and Mills, Gordon B and Shaw, Kenna R Mills and Ozenberger, Brad A and Ellrott, Kyle and Shmulevich, Ilya and Sander, Chris and Stuart, Joshua M and Cancer Genome Atlas Research Network and others},
  journal={Nature genetics},
  volume={45},
  number={10},
  pages={1113},
  year={2013},
  publisher={Nature Publishing Group}
}

@misc{coad2016,
  author       = {Shanah, Kirk and Cheryl A., Sadow and J. Keith, Smith and Seth, Levine and Charles, Roche and  Ermalinda, Bonaccio and Joe, Filippini},
  title        = {Radiology Data from The Cancer Genome Atlas Colon Adenocarcinoma [TCGA-COAD] collection},
  year         = 2016,
  publisher    = {The Cancer Imaging Archive}
}
  %url          = {http://doi.org/10.7937/K9/TCIA.2016.HJJHBOXZ}

@misc{read2016,
  author       = {Shanah, Kirk and Cheryl A., Sadow and Seth, Levine},
  title        = {Radiology Data from The Cancer Genome Atlas Rectum Adenocarcinoma [TCGA-READ] collection},
  year         = 2016,
  publisher    = {The Cancer Imaging Archive}
}
%  url          = {http://doi.org/10.7937/K9/TCIA.2016.F7PPNPNU}

% CRC-TP
@article{javed2020cellular,
  title={Cellular community detection for tissue phenotyping in colorectal cancer histology images},
  author={Javed, Sajid and Mahmood, Arif and Fraz, Muhammad Moazam and Koohbanani, Navid Alemi and Benes, Ksenija and Tsang, Yee-Wah and Hewitt, Katherine and Epstein, David and Snead, David and Rajpoot, Nasir},
  journal={Medical Image Analysis},
  pages={101696},
  year={2020},
  publisher={Elsevier}
}

% Kather 19
@article{kather2019predicting,
  title={Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study},
  author={Kather, Jakob Nikolas and Krisam, Johannes and Charoentong, Pornpimol and Luedde, Tom and Herpel, Esther and Weis, Cleo-Aron and Gaiser, Timo and Marx, Alexander and Valous, Nektarios A and Ferber, Dyke and others},
  journal={PLoS medicine},
  volume={16},
  number={1},
  year={2019},
  publisher={Public Library of Science}
}

% Kather-16
@article{kather2016multi,
  title={Multi-class texture analysis in colorectal cancer histology},
  author={Kather, Jakob Nikolas and Weis, Cleo-Aron and Bianconi, Francesco and Melchers, Susanne M and Schad, Lothar R and Gaiser, Timo and Marx, Alexander and Z{\"o}llner, Frank Gerrit},
  journal={Scientific reports},
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% #####################################################################
% experimental set-up #################################################
% #####################################################################




% #####################################################################
% results #############################################################
% #####################################################################

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