midl-samplebibliography.bib

@article{esteban2017mriqc,
  title={MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites},
  author={Esteban, Oscar and Birman, Daniel and Schaer, Marie and Koyejo, Oluwasanmi O and Poldrack, Russell A and Gorgolewski, Krzysztof J},
  journal={PloS one},
  volume={12},
  number={9},
  pages={e0184661},
  year={2017},
  publisher={Public Library of Science San Francisco, CA USA}
}

@article{bosse2017deep,
  title={Deep neural networks for no-reference and full-reference image quality assessment},
  author={Bosse, Sebastian and Maniry, Dominique and M{\"u}ller, Klaus-Robert and Wiegand, Thomas and Samek, Wojciech},
  journal={IEEE Transactions on image processing},
  volume={27},
  number={1},
  pages={206--219},
  year={2017},
  publisher={IEEE}
}

@article{bianco2018use,
  title={On the use of deep learning for blind image quality assessment},
  author={Bianco, Simone and Celona, Luigi and Napoletano, Paolo and Schettini, Raimondo},
  journal={Signal, Image and Video Processing},
  volume={12},
  number={2},
  pages={355--362},
  year={2018},
  publisher={Springer}
}

@article{hosu2020koniq,
  title={KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment},
  author={Hosu, Vlad and Lin, Hanhe and Sziranyi, Tamas and Saupe, Dietmar},
  journal={IEEE Transactions on Image Processing},
  volume={29},
  pages={4041--4056},
  year={2020},
  publisher={IEEE}
}

@inproceedings{yu2017image,
  title={Image quality classification for DR screening using deep learning},
  author={Yu, FengLi and Sun, Jing and Li, Annan and Cheng, Jun and Wan, Cheng and Liu, Jiang},
  booktitle={2017 39th Annual Int. Conf. of the IEEE Engineering in Medicine and Biology Society (EMBC)},
  pages={664--667},
  year={2017},
  organization={IEEE}
}

@article{hou2014blind,
  title={Blind image quality assessment via deep learning},
  author={Hou, Weilong and Gao, Xinbo and Tao, Dacheng and Li, Xuelong},
  journal={IEEE transactions on neural networks and learning systems},
  volume={26},
  number={6},
  pages={1275--1286},
  year={2014},
  publisher={IEEE}
}

@article{higaki2019improvement,
  title={Improvement of image quality at CT and MRI using deep learning},
  author={Higaki, Toru and Nakamura, Yuko and Tatsugami, Fuminari and Nakaura, Takeshi and Awai, Kazuo},
  journal={Japanese journal of radiology},
  volume={37},
  number={1},
  pages={73--80},
  year={2019},
  publisher={Springer}
}

@article{mccormick2014itk,
  title={ITK: enabling reproducible research and open science},
  author={McCormick, Matthew Michael and Liu, Xiaoxiao and Ibanez, Luis and Jomier, Julien and Marion, Charles},
  journal={Frontiers in neuroinformatics},
  volume={8},
  pages={13},
  year={2014},
  publisher={Frontiers}
}

@article{perez-garcia_torchio_2021,
   title = {TorchIO: a Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning},
   journal = {Computer Methods and Programs in Biomedicine},
   pages = {106236},
   year = {2021},
   issn = {0169-2607},
   doi = {https://doi.org/10.1016/j.cmpb.2021.106236},
   url = {https://www.sciencedirect.com/science/article/pii/S0169260721003102},
   author = {P{\'e}rez-Garc{\'i}a, Fernando and Sparks, Rachel and Ourselin, S{\'e}bastien},
}

@article{paulsen2014clinical,
  title={Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: a decade of the PREDICT-HD study},
  author={Paulsen, Jane S and Long, Jeffrey D and Johnson, Hans J and Aylward, Elizabeth H and Ross, Christopher A and Williams, Janet K and Nance, Martha A and Erwin, Cheryl J and Westervelt, Holly K and Harrington, Deborah Lynn and others},
  journal={Front. in aging neuroscience},
  volume={6},
  pages={78},
  year={2014},
  publisher={Frontiers}
}

@inproceedings{butskova2021adversarial,
  title={Adversarial Bayesian Optimization for Quantifying Motion Artifact Within MRI},
  author={Butskova, Anastasia and Juhl, Rain and Zuki{\'c}, D{\v{z}}enan and Chaudhary, Aashish and Pohl, Kilian M and Zhao, Qingyu},
  booktitle={Int. Workshop on PRedictive Intellig. In MEdicine},
  pages={83--92},
  year={2021},
  organization={Springer}
}

@incollection{rekik_adversarial_2021,
	address = {Cham},
	title = {Adversarial {Bayesian} {Optimization} for {Quantifying} {Motion} {Artifact} {Within} {MRI}},
	volume = {12928},
	isbn = {978-3-030-87601-2 978-3-030-87602-9},
	url = {https://link.springer.com/10.1007/978-3-030-87602-9_8},
	language = {en},
	urldate = {2022-04-21},
	booktitle = {Predictive {Intelligence} in {Medicine}},
	publisher = {Springer International Publishing},
	author = {Butskova, Anastasia and Juhl, Rain and Zukić, Dženan and Chaudhary, Aashish and Pohl, Kilian M. and Zhao, Qingyu},
	editor = {Rekik, Islem and Adeli, Ehsan and Park, Sang Hyun and Schnabel, Julia},
	year = {2021},
	doi = {10.1007/978-3-030-87602-9_8},
	note = {Series Title: Lecture Notes in Computer Science},
	pages = {83--92},
}