%% This BibTeX bibliography file was created using BibDesk.
%% http://bibdesk.sourceforge.net/

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@article{ZhangJ2021,
   author = {Jie Zhang and Bowen Zheng and Ang Gao and Xin Feng and Dong Liang and Xiaojing Long},
   doi = {10.1016/j.mri.2021.02.001},
   issn = {18735894},
   journal = {Magnetic Resonance Imaging},
   month = {5},
   pages = {119-126},
   pmid = {33588019},
   publisher = {Elsevier Inc.},
   title = {{A {3D} densely connected convolution neural network with connection-wise attention mechanism for {Alzheimer's} disease classification}},
   volume = {78},
   year = {2021},
}

@article{ZhangX2021,
   title={{An Explainable {3D} Residual Self-Attention Deep Neural Network for Joint Atrophy Localization and {Alzheimer’s} Disease Diagnosis using Structural {MRI}}},
   ISSN={2168-2208},
   url={http://dx.doi.org/10.1109/JBHI.2021.3066832},
   DOI={10.1109/jbhi.2021.3066832},
   journal={IEEE Journal of Biomedical and Health Informatics},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Zhang, Xin and Han, Liangxiu and Zhu, Wenyong and Sun, Liang and Zhang, Daoqiang},
   year={2021},
}

@INPROCEEDINGS{DanJin2019,
  author={Jin, Dan and Xu, Jian and Zhao, Kun and Hu, Fangzhou and Yang, Zhengyi and Liu, Bing and Jiang, Tianzi and Liu, Yong},
  booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)}, 
  title={{Attention-based {3D} Convolutional Network for {Alzheimer’s} Disease Diagnosis and Biomarkers Exploration}}, 
  year={2019},
  volume={},
  number={},
  pages={1047-1051},
  doi={10.1109/ISBI.2019.8759455}
  }
  
  @INPROCEEDINGS{Ullanat2020,
  author={Ullanat, Varun and Balamurali, Vinay and Rao, Ananya},
  booktitle={2020 IEEE-EMBS Conference on Biomedical Engineering and Sciences (IECBES)}, 
  title={{A Novel Residual {3-D} Convolutional Network for {Alzheimer's} disease diagnosis based on raw {MRI} scans}}, 
  year={2021},
  volume={},
  number={},
  pages={82-87},
  doi={10.1109/IECBES48179.2021.9398800}
  }
  
@INPROCEEDINGS{Bicacro2012,
  author={Bicacro, Eduardo and Silveira, Margarida and Marques, Jorge S. and Costa, Durval C.},
  booktitle={2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)}, 
  title={{{3D} brain image-based diagnosis of {Alzheimer's} disease: Bringing medical vision into feature selection}}, 
  year={2012},
  volume={},
  number={},
  pages={134-137},
  doi={10.1109/ISBI.2012.6235502}
  }
  
@mastersthesis{BicacroThesis,
  author       = {Eduardo Bicacro and Margarida C Silveira and Jorge S Marques}, 
  title        = {{{Alzheimer’s} Disease Diagnosis using {3D} Brain Images: Exploring new grounds on features extraction and selection}},
  school       = {Instituto Superior Técnico},
  year         = 2011,
  month        = 11,
}
  
@article{Turkan2021,
   author = {Yasemin Turkan and F. Boray Tek},
   doi = {10.1109/UBMK52708.2021.9558882},
   isbn = {978-1-6654-2908-5},
   journal = {2021 6th International Conference on Computer Science and Engineering (UBMK)},
   month = {9},
   pages = {1-6},
   publisher = {IEEE},
   booktitle = {},
   title = {{Convolutional Attention Network for {MRI-based} {Alzheimer’s} Disease Classification and its Interpretability Analysis}},
   url = {https://ieeexplore.ieee.org/document/9558882/},
   year = {2021},
}

@INPROCEEDINGS{Korolev2017,
  author={Korolev, Sergey and Safiullin, Amir and Belyaev, Mikhail and Dodonova, Yulia},
  booktitle={2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)}, 
  title={{Residual and plain convolutional neural networks for {3D} brain {MRI} classification}}, 
  year={2017},
  volume={},
  number={},
  pages={835-838},
  doi={10.1109/ISBI.2017.7950647}
  }
  
@article{ZhangY2021,
title = {{Diagnosis of {Alzheimer's} Disease Based on Regional Attention with {sMRI} Gray Matter Slices}},
journal = {Journal of Neuroscience Methods},
pages = {109376},
year = {2021},
issn = {0165-0270},
doi = {https://doi.org/10.1016/j.jneumeth.2021.109376},
url = {https://www.sciencedirect.com/science/article/pii/S0165027021003113},
author = {Yanteng Zhang and Qizhi Teng and Yuyang Liu and Yan Liu and Xiaohai He},
}

@article{Sun2021,
   author = {Haijing Sun and Anna Wang and Wenhui Wang and Chen Liu},
   doi = {10.3390/s21124182},
   issn = {14248220},
   issue = {12},
   journal = {Sensors},
   month = {6},
   pmid = {34207145},
   publisher = {MDPI AG},
   title = {{An improved deep residual network prediction model for the early diagnosis of {Alzheimer’s} disease}},
   volume = {21},
   year = {2021},
}


@Article{Liang2021,
AUTHOR = {Liang, Shuang and Gu, Yu},
TITLE = {{Computer-Aided Diagnosis of {Alzheimer’s} Disease through Weak Supervision Deep Learning Framework with Attention Mechanism}},
JOURNAL = {Sensors},
VOLUME = {21},
YEAR = {2021},
NUMBER = {1},
ARTICLE-NUMBER = {220},
URL = {https://www.mdpi.com/1424-8220/21/1/220},
ISSN = {1424-8220},
DOI = {10.3390/s21010220}
}

@article{Basaia2019,
title = {{Automated classification of {Alzheimer's} disease and mild cognitive impairment using a single {MRI} and deep neural networks}},
journal = {NeuroImage: Clinical},
volume = {21},
pages = {101645},
year = {2019},
issn = {2213-1582},
doi = {https://doi.org/10.1016/j.nicl.2018.101645},
url = {https://www.sciencedirect.com/science/article/pii/S2213158218303930},
author = {Silvia Basaia and Federica Agosta and Luca Wagner and Elisa Canu and Giuseppe Magnani and Roberto Santangelo and Massimo Filippi},
}

@INPROCEEDINGS{He2016,
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={{Deep Residual Learning for Image Recognition}}, 
  year={2016},
  volume={},
  number={},
  pages={770-778},
  doi={10.1109/CVPR.2016.90}
  }
  
@ARTICLE{Li2020,
  author={Li, Liu and Xu, Mai and Liu, Hanruo and Li, Yang and Wang, Xiaofei and Jiang, Lai and Wang, Zulin and Fan, Xiang and Wang, Ningli},
  journal={IEEE Transactions on Medical Imaging}, 
  title={{A Large-Scale Database and a {CNN} Model for Attention-Based Glaucoma Detection}}, 
  year={2020},
  volume={39},
  number={2},
  pages={413-424},
  doi={10.1109/TMI.2019.2927226}
}
  
@INPROCEEDINGS{Yu2017,
  author={Yu, Youngjae and Choi, Jongwook and Kim, Yeonhwa and Yoo, Kyung and Lee, Sang-Hun and Kim, Gunhee},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition ({CVPR})}, 
  title={{Supervising Neural Attention Models for Video Captioning by Human Gaze Data}}, 
  year={2017},
  volume={},
  number={},
  pages={6119-6127},
  doi={10.1109/CVPR.2017.648}
}
  
@inproceedings{Mnih2014,
author = {Mnih, Volodymyr and Heess, Nicolas and Graves, Alex and Kavukcuoglu, Koray},
title = {{Recurrent Models of Visual Attention}},
year = {2014},
publisher = {MIT Press},
address = {Cambridge, MA, USA},
booktitle = {Proceedings of the 27th International Conference on Neural Information Processing Systems - Volume 2},
pages = {2204–2212},
numpages = {9},
location = {Montreal, Canada},
series = {NIPS'14},
}

@article{Ypsilantis2017,
  title={{Learning what to look in chest {X-rays} with a recurrent visual attention model}},
  author={Petros-Pavlos Ypsilantis and G. Montana},
  journal={ArXiv},
  year={2017},
  volume={abs/1701.06452}
}

@inproceedings{Ba2015,
  author= {Jimmy Ba and Volodymyr Mnih and Koray Kavukcuoglu},
  editor= {Yoshua Bengio and Yann LeCun},
  title= {{Multiple Object Recognition with Visual Attention}},
  booktitle= {3rd International Conference on Learning Representations, {ICLR} 2015,
               San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings},
  year      = {2015},
  url       = {http://arxiv.org/abs/1412.7755},
  timestamp = {Thu, 25 Jul 2019 14:25:39 +0200},
  biburl    = {https://dblp.org/rec/journals/corr/BaMK14.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@misc{khosla2020,
      title={{Neural encoding with visual attention}}, 
      author={Meenakshi Khosla and Gia H. Ngo and Keith Jamison and Amy Kuceyeski and Mert R. Sabuncu},
      year={2020},
      eprint={2010.00516},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

@article{Xie2021,
title = {{A survey on incorporating domain knowledge into deep learning for medical image analysis}},
journal = {Medical Image Analysis},
volume = {69},
pages = {101985},
year = {2021},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2021.101985},
url = {https://www.sciencedirect.com/science/article/pii/S1361841521000311},
author = {Xiaozheng Xie and Jianwei Niu and Xuefeng Liu and Zhengsu Chen and Shaojie Tang and Shui Yu},
keywords = {Medical image analysis, Medical domain knowledge, Deep learning models},
}

@article{Amir2020,
title = {{Deep learning to detect {Alzheimer's} disease from neuroimaging: A systematic literature review}},
journal = {Computer Methods and Programs in Biomedicine},
volume = {187},
pages = {105242},
year = {2020},
issn = {0169-2607},
doi = {https://doi.org/10.1016/j.cmpb.2019.105242},
url = {https://www.sciencedirect.com/science/article/pii/S0169260719310946},
author = {Amir Ebrahimighahnavieh and Suhuai Luo and Raymond Chiong},
}


@article{bondi_edmonds_salmon_2017, 
title={{{Alzheimer’s} Disease: Past, Present, and Future}},
volume={23}, 
DOI={10.1017/S135561771700100X}, 
number={9-10}, 
journal={Journal of the International Neuropsychological Society}, 
publisher={Cambridge University Press}, 
author={Bondi, Mark W. and Edmonds, Emily C. and Salmon, David P.}, 
year={2017}, 
pages={818–831}
}

@techreport{WorldAlzheimersReport2021,
   title = {{World {Alzheimer} Report 2021: Journey through the diagnosis of dementia}},
   author={Serge Gauthier and Pedro Rosa-Neto and José A. Morais and Claire Webster}, 
   year={2021}, 
   institution={Alzheimer's Disease International},
}

@article{2020Alzheimer'sdiseasefactsfigures,
   author = {Joseph Gaugler and Bryan James and Tricia Johnson and Allison Marin and Jennifer Weuve},
   doi = {10.1002/alz.12068},
   issn = {15525279},
   issue = {3},
   journal = {Alzheimer's and Dementia},
   pages = {391-460},
   pmid = {32157811},
   title = {{2020 {Alzheimer's} Disease facts and figures}},
   volume = {16},
   year = {2020},
}

@article{pathogenesis2017,
   author = {Alfredo Sanabria-Castro and Ileana Alvarado-Echeverría and Cecilia Monge-Bonilla},
   doi = {10.1159/000464422},
   issn = {09763260},
   issue = {1},
   journal = {Annals of Neurosciences},
   pages = {46-54},
   pmid = {28588356},
   title = {{Molecular pathogenesis of {Alzheimer’s} disease: An update}},
   volume = {24},
   year = {2017},
}

@article{Marcus2014,
   author = {Charles Marcus and Esther Mena and Rathan M. Subramaniam},
   doi = {10.1097/RLU.0000000000000547},
   issn = {15360229},
   issue = {10},
   journal = {Clinical Nuclear Medicine},
   pages = {e413-e426},
   pmid = {25199063},
   title = {{Brain {PET} in the diagnosis of {Alzheimer's} disease}},
   volume = {39},
   year = {2014},
}

@article{Bloudek2011,
   author = {Lisa M. Bloudek and D. Eldon Spackman and Michael Blankenburg and Sean D. Sullivan},
   doi = {10.3233/JAD-2011-110458},
   issn = {18758908},
   issue = {4},
   journal = {Journal of Alzheimer's Disease},
   pages = {627-645},
   publisher = {IOS Press},
   title = {{Review and meta-analysis of biomarkers and diagnostic imaging in {Alzheimer's} disease}},
   volume = {26},
   year = {2011},
}

@techreport{WorldAlzheimersReport2015,
   author = {Martin Prince and Ander Wimo and Maëlenn Guerchet and Gemma-Claire Ali and Yu-Tzu Wu and Matthew Prina},
   title = {{World {Alzheimer} Report 2015: {The} Global Impact of Dementia}},
   url = {https://www.alz.co.uk/research/WorldAlzheimerReport2015.pdf},
   year = {2015},
   institution = {Alzheimer’s Disease International},
}

@techreport{WorldAlzheimersReport2018,
   author = {Christina Patterson},
   title = {{World {Alzheimer} Report 2018 The State of the Art of Dementia Research: New Frontiers}},
   url = {https://www.alzint.org/resource/world-alzheimer-report-2018/},
   year = {2018},
   institution = {Alzheimer’s Disease International},
}

@InProceedings{CBAM,
    author={Woo, Sanghyun and Park, Jongchan
    and Lee, Joon-Young and Kweon, In So},
    editor={Ferrari, Vittorio
    and Hebert, Martial
    and Sminchisescu, Cristian
    and Weiss, Yair},
    title={{{CBAM}: Convolutional Block Attention Module}},
    booktitle={Computer Vision -- ECCV 2018},
    year={2018},
    publisher={Springer International Publishing},
    address={Cham},
    pages={3--19},
    isbn={978-3-030-01234-2}
}

@misc{adni, 
 title={{{Alzheimer's} Disease neuroimaging initiative}}, 
 journal={ADNI},
 note={\url{http://adni.loni.usc.edu/}, accessed 2022-01-03}
} 

@manual{tobii,
title = {{Tobii T/X series Eye Trackers User Manual}},
date = {June 2010},
language = {English},
version = {Revision 2.1},
organization = {Tobii\textregistered Technology},
pages = {17},
}

@manual{tobii_leaflet,
title = {{Tobii T60 \& T120 Eye Trackers User Manual}},
date = {},
language = {English},
version = {},
organization = {Tobii\textregistered Technology},
pages = {},
}
 

@INPROCEEDINGS{squeeze,
  author={Hu, Jie and Shen, Li and Sun, Gang},
  booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
  title={{Squeeze-and-Excitation Networks}}, 
  year={2018},
  volume={},
  number={},
  pages={7132-7141},
  doi={10.1109/CVPR.2018.00745}
  }
  
@incollection{KHAN2016,
title = {{Chapter 2 - Clinical Diagnosis of {Alzheimer’s} Disease}},
editor = {T.K. Khan},
booktitle = {Biomarkers in Alzheimer's Disease},
publisher = {Academic Press},
pages = {27-48},
year = {2016},
isbn = {978-0-12-804832-0},
doi = {https://doi.org/10.1016/B978-0-12-804832-0.00002-X},
url = {https://www.sciencedirect.com/science/article/pii/B978012804832000002X},
author = {T.K. Khan},
keywords = {clinical AD diagnosis, preclinical AD, mild cognitive impairment (MCI), mini-mental state examination (MMSE), clinical dementia rating (CDR), NINCDS-ADRDA, IWG AD criteria, Alzheimer’s disease assessment scale (ADAS), AD risk factors},
}


@INPROCEEDINGS{MLClassifiers,
  author={Kasani, Payam Hosseinzadeh and Kasani, Sara Hosseinzadeh and Kim, Yeshin and Yun, Cheol-Heui and Choi, Seong Hye and Jang, Jae-Won},
  booktitle={2021 International Conference on Information and Communication Technology Convergence (ICTC)}, 
  title={{An Evaluation of Machine Learning Classifiers for Prediction of {Alzheimer's} Disease, Mild Cognitive Impairment and Normal Cognition}}, 
  year={2021},
  volume={},
  number={},
  pages={362-367},
  doi={10.1109/ICTC52510.2021.9620780}
  }
  
@inproceedings{ShowAttendandTell,
author = {Xu, Kelvin and Ba, Jimmy Lei and Kiros, Ryan and Cho, Kyunghyun and Courville, Aaron and Salakhutdinov, Ruslan and Zemel, Richard S. and Bengio, Yoshua},
title = {{Show, Attend and Tell: Neural Image Caption Generation with Visual Attention}},
year = {2015},
publisher = {JMLR.org},
booktitle = {Proceedings of the 32nd International Conference on Machine Learning - Volume 37},
pages = {2048–2057},
numpages = {10},
location = {Lille, France},
series = {ICML'15}
}

@article{PET_CNN,
    doi = {10.1371/journal.pone.0258214},
    author = {Lee, Seung-Yeon AND Kang, Hyeon AND Jeong, Jong-Hun AND Kang, Do-young},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {{Performance evaluation in [18F]Florbetaben brain PET images classification using {3D} Convolutional Neural Network}},
    year = {2021},
    month = {10},
    volume = {16},
    url = {https://doi.org/10.1371/journal.pone.0258214},
    pages = {1-16},
    number = {10},
}

@ARTICLE{PET_CNN_RNN,
AUTHOR={Liu, Manhua and Cheng, Danni and Yan, Weiwu},   
TITLE={{Classification of {Alzheimer’s} Disease by Combination of Convolutional and Recurrent Neural Networks Using {FDG-PET}
Images}},      
JOURNAL={Frontiers in Neuroinformatics},      
VOLUME={12},      
YEAR={2018},      
URL={https://www.frontiersin.org/article/10.3389/fninf.2018.00035},       
DOI={10.3389/fninf.2018.00035},      
ISSN={1662-5196}  
}

@article{Yang2020,
	doi = {10.1088/1742-6596/1693/1/012173},
	url = {https://doi.org/10.1088/1742-6596/1693/1/012173},
	year = 2020,
	month = {dec},
	publisher = {{IOP} Publishing},
	volume = {1693},
	number = {1},
	pages = {012173},
	author = {Xiao Yang},
	title = {{An Overview of the Attention Mechanisms in Computer Vision}},
	journal = {Journal of Physics: Conference Series},
}

@inproceedings{BAM,
  title={{BAM: Bottleneck Attention Module}},
  author={Jongchan Park and Sanghyun Woo and Joon-Young Lee and In-So Kweon},
  booktitle={BMVC},
  year={2018}
}

@ARTICLE{DeepAM,
  author={Wang, Wenguan and Shen, Jianbing},
  journal={IEEE Transactions on Image Processing}, 
  title={{Deep Visual Attention Prediction}}, 
  year={2018},
  volume={27},
  number={5},
  pages={2368-2378},
  doi={10.1109/TIP.2017.2787612}
  }
  
@INPROCEEDINGS{Grad-CAM,
author={Selvaraju, Ramprasaath R. and Cogswell, Michael and Das, Abhishek and Vedantam, Ramakrishna and Parikh, Devi and Batra, Dhruv},
booktitle={2017 IEEE International Conference on Computer Vision (ICCV)}, 
title={{{Grad-CAM}: Visual Explanations from Deep Networks via Gradient-Based Localization}}, 
year={2017},
volume={},
number={},
pages={618-626},
doi={10.1109/ICCV.2017.74}
}

@ARTICLE{PET_MRI_comparison,
  title     = {{Comparing {PET} and {MRI} biomarkers predicting cognitive decline in preclinical {Alzheimer's} disease}},
  author    = "Mayblyum, Danielle V and Becker, J Alex and Jacobs, Heidi I L
               and Buckley, Rachel F and Schultz, Aaron P and Sepulcre, Jorge
               and Sanchez, Justin S and Rubinstein, Zoe B and Katz, Samantha R
               and Moody, Kirsten A and Vannini, Patrizia and Papp, Kathryn V
               and Rentz, Dorene M and Price, Julie C and Sperling, Reisa A and
               Johnson, Keith A and Hanseeuw, Bernard J",
  abstract  = "OBJECTIVE: To compare how structural MRI, Fluorodeoxyglucose
               (FDG), and Flortaucipir (FTP) PET signal predict cognitive
               decline in high-amyloid versus low-amyloid participants with the
               goal of determining which biomarker combination would result in
               the highest increase of statistical power for prevention trials.
               METHODS: In this prospective cohort study, we analyzed data from
               clinically-normal adults from the Harvard Aging Brain Study with
               MRI, FDG, FTP, and PiB-PET acquired within a year, and
               prospective cognitive evaluations over a mean three-year
               follow-up. We focused analyses on pre-defined
               regions-of-interest: inferior temporal, isthmus cingulate,
               hippocampus, and entorhinal cortex. Cognition was assessed using
               the Preclinical Alzheimer's Cognitive Composite (PACC5). We
               evaluated the association between biomarkers and cognitive
               decline using linear-mixed-effect models with random intercepts
               and slopes, adjusting for demographics. We generated power
               curves simulating prevention trials. RESULTS: Data from 131
               participants [52 females, 73.98$\pm$8.29 years old] were
               analyzed in the study. In separate models, most biomarkers had a
               closer association with cognitive decline in the high-PiB
               compared to the low-PiB participants. A backward stepwise
               regression including all biomarkers demonstrated that only
               neocortical PiB, entorhinal FTP, and entorhinal FDG were
               independent predictors of subsequent cognitive decline. Power
               analyses revealed that using both high-PiB and low entorhinal
               FDG as inclusion criteria reduced 3-fold the number of
               participants needed in a hypothetical trial compared to using
               only high-PiB. DISCUSSION: In preclinical Alzheimer's disease,
               entorhinal hypometabolism is a strong and independent predictor
               of subsequent cognitive decline, making FDG a potentially useful
               biomarker to increase power in clinical trials. CLASSIFICATION
               OF EVIDENCE: This study provides Class II evidence that in
               people with preclinical Alzheimer's disease, entorhinal
               hypometabolism identified by FDG-PET is predictive of subsequent
               cognitive decline.",
  journal   = "Neurology",
  publisher = "Ovid Technologies (Wolters Kluwer Health)",
  volume    =  96,
  number    =  24,
  pages     = "e2933--e2943",
  month     =  may,
  year      =  2021,
  language  = "en"
}

@ARTICLE{STA-CNN,
  author={Yang, Hao and Yuan, Chunfeng and Zhang, Li and Sun, Yunda and Hu, Weiming and Maybank, Stephen J.},
  journal={IEEE Transactions on Image Processing}, 
  title={{{STA-CNN}: Convolutional Spatial-Temporal Attention Learning for Action Recognition}}, 
  year={2020},
  volume={29},
  number={},
  pages={5783-5793},
  doi={10.1109/TIP.2020.2984904}}


@InProceedings{identity_mapping,
author="He, Kaiming
and Zhang, Xiangyu
and Ren, Shaoqing
and Sun, Jian",
editor="Leibe, Bastian
and Matas, Jiri
and Sebe, Nicu
and Welling, Max",
title={{Identity Mappings in Deep Residual Networks}},
booktitle="Computer Vision -- ECCV 2016",
year="2016",
publisher="Springer International Publishing",
address="Cham",
pages="630--645",
isbn="978-3-319-46493-0"
}

@INPROCEEDINGS{residual_attention,
  author={Wang, Fei and Jiang, Mengqing and Qian, Chen and Yang, Shuo and Li, Cheng and Zhang, Honggang and Wang, Xiaogang and Tang, Xiaoou},
  booktitle={2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={{Residual Attention Network for Image Classification}}, 
  year={2017},
  volume={},
  number={},
  pages={6450-6458},
  doi={10.1109/CVPR.2017.683}}
  
 @INPROCEEDINGS{non_local_attention,
  author={Wang, Xiaolong and Girshick, Ross and Gupta, Abhinav and He, Kaiming},
  booktitle={2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition}, 
  title={{Non-local Neural Networks}}, 
  year={2018},
  volume={},
  number={},
  pages={7794-7803},
  doi={10.1109/CVPR.2018.00813}}
  
  
@article{saliency_maps, 
title={{Methods for comparing scanpaths and saliency maps: Strengths and weaknesses}}, 
volume={45},
DOI={10.3758/s13428-012-0226-9}, 
number={1}, 
journal={Behavior Research Methods}, 
author={Le Meur, Olivier and Baccino, Thierry}, year={2012}, 
pages={251–266}} 
  
  
@misc{guided_BP,
  doi = {10.48550/ARXIV.1412.6806},
  url = {https://arxiv.org/abs/1412.6806},
  author = {Springenberg, Jost Tobias and Dosovitskiy, Alexey and Brox, Thomas and Riedmiller, Martin},
  keywords = {Machine Learning (cs.LG), Computer Vision and Pattern Recognition (cs.CV), Neural and Evolutionary Computing (cs.NE), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {{Striving for Simplicity: The All Convolutional Net}},
  publisher = {arXiv},
  year = {2014},
  copyright = {arXiv.org perpetual, non-exclusive license}
}
 
 
@mastersthesis{MorgadoThesis,
  author       = {Pedro Morgado and Margarida C. da Silveira and Jorge S. Marques}, 
  title        = {{Automated Diagnosis of Alzheimer’s Disease using {PET} Images: A study of alternative procedures for feature extraction and selection}},
  school       = {Instituto Superior Técnico},
  year         = 2012,
  month        = 9,
}


---------------------------------

@INPROCEEDINGS{FDG_PET_categories,
  title           = {{Deep-learning-based classification of {FDG-PET} data for Alzheimer's disease categories}},
  booktitle       = "13th International Conference on Medical Information Processing and Analysis",
  author          = "Singh, Shibani and Srivastava, Anant and Mi, Liang and
                     Chen, Kewei and Wang, Yalin and Caselli, Richard J and
                     Goradia, Dhruman and Reiman, Eric M",
  editor          = "Brieva, Jorge and Garc{\'\i}a, Juan David and Lepore,
                     Natasha and Romero, Eduardo",
  publisher       = "SPIE",
  month           =  nov,
  year            =  2017,
  conference      = "13th International Symposium on Medical Information Processing and Analysis",
  location        = "San Andres Island, Colombia"
}

@ARTICLE{cognitive_signature,
  title     = {{Cognitive signature of brain {FDG}-{PET} based on deep learning: domain transfer from Alzheimer's disease to Parkinson's disease}},
  author    = "Choi, Hongyoon and Kim, Yu Kyeong and Yoon, Eun Jin and Lee,
               Jee-Young and Lee, Dong Soo and {Alzheimer's Disease
               Neuroimaging Initiative}",
  abstract  = "PURPOSE: Although functional brain imaging has been used for the
               early and objective assessment of cognitive dysfunction, there
               is a lack of generalized image-based biomarker which can
               evaluate individual's cognitive dysfunction in various
               disorders. To this end, we developed a deep learning-based
               cognitive signature of FDG brain PET adaptable for Parkinson's
               disease (PD) as well as Alzheimer's disease (AD). METHODS: A
               deep learning model for discriminating AD from normal controls
               (NCs) was built by a training set consisting of 636 FDG PET
               obtained from Alzheimer's Disease Neuroimaging Initiative
               database. The model was directly transferred to images of mild
               cognitive impairment (MCI) patients (n = 666) for identifying
               who would rapidly convert to AD and another independent cohort
               consisting of 62 PD patients to differentiate PD patients with
               dementia. The model accuracy was measured by area under curve
               (AUC) of receiver operating characteristic (ROC) analysis. The
               relationship between all images was visualized by
               two-dimensional projection of the deep learning-based features.
               The model was also designed to predict cognitive score of the
               subjects and validated in PD patients. Cognitive
               dysfunction-related regions were visualized by feature maps of
               the deep CNN model. RESULTS: AUC of ROC for differentiating AD
               from NC was 0.94 (95\% CI 0.89-0.98). The transfer of the model
               could differentiate MCI patients who would convert to AD (AUC =
               0.82) and PD with dementia (AUC = 0.81). The two-dimensional
               projection mapping visualized the degree of cognitive
               dysfunction compared with normal brains regardless of different
               disease cohorts. Predicted cognitive score, an output of the
               model, was highly correlated with the mini-mental status exam
               scores. Individual cognitive dysfunction-related regions
               included cingulate and high frontoparietal cortices, while they
               showed individual variability. CONCLUSION: The deep
               learning-based cognitive function evaluation model could be
               successfully transferred to multiple disease domains. We suggest
               that this approach might be extended to an objective cognitive
               signature that provides quantitative biomarker for cognitive
               dysfunction across various neurodegenerative disorders.",
  journal   = "Eur. J. Nucl. Med. Mol. Imaging",
  publisher = "Springer Science and Business Media LLC",
  volume    =  47,
  number    =  2,
  pages     = "403--412",
  month     =  feb,
  year      =  2020,
  keywords  = "Deep learning; Dementia; FDG PET; Parkinson disease; Transfer
               learning",
  language  = "en"
}

@ARTICLE{pet_prediction,
  title     = {{A deep learning model to predict a diagnosis of Alzheimer disease by using {18F-FDG} {PET} of the brain}},
  author    = "Ding, Yiming and Sohn, Jae Ho and Kawczynski, Michael G and
               Trivedi, Hari and Harnish, Roy and Jenkins, Nathaniel W and
               Lituiev, Dmytro and Copeland, Timothy P and Aboian, Mariam S and
               Mari Aparici, Carina and Behr, Spencer C and Flavell, Robert R
               and Huang, Shih-Ying and Zalocusky, Kelly A and Nardo, Lorenzo
               and Seo, Youngho and Hawkins, Randall A and Hernandez Pampaloni,
               Miguel and Hadley, Dexter and Franc, Benjamin L",
  abstract  = "Purpose To develop and validate a deep learning algorithm that
               predicts the final diagnosis of Alzheimer disease (AD), mild
               cognitive impairment, or neither at fluorine 18 (18F)
               fluorodeoxyglucose (FDG) PET of the brain and compare its
               performance to that of radiologic readers. Materials and Methods
               Prospective 18F-FDG PET brain images from the Alzheimer's
               Disease Neuroimaging Initiative (ADNI) (2109 imaging studies
               from 2005 to 2017, 1002 patients) and retrospective independent
               test set (40 imaging studies from 2006 to 2016, 40 patients)
               were collected. Final clinical diagnosis at follow-up was
               recorded. Convolutional neural network of InceptionV3
               architecture was trained on 90\% of ADNI data set and tested on
               the remaining 10\%, as well as the independent test set, with
               performance compared to radiologic readers. Model was analyzed
               with sensitivity, specificity, receiver operating characteristic
               (ROC), saliency map, and t-distributed stochastic neighbor
               embedding. Results The algorithm achieved area under the ROC
               curve of 0.98 (95\% confidence interval: 0.94, 1.00) when
               evaluated on predicting the final clinical diagnosis of AD in
               the independent test set (82\% specificity at 100\%
               sensitivity), an average of 75.8 months prior to the final
               diagnosis, which in ROC space outperformed reader performance
               (57\% [four of seven] sensitivity, 91\% [30 of 33] specificity;
               P < .05). Saliency map demonstrated attention to known areas of
               interest but with focus on the entire brain. Conclusion By using
               fluorine 18 fluorodeoxyglucose PET of the brain, a deep learning
               algorithm developed for early prediction of Alzheimer disease
               achieved 82\% specificity at 100\% sensitivity, an average of
               75.8 months prior to the final diagnosis. \copyright{} RSNA,
               2018 Online supplemental material is available for this article.
               See also the editorial by Larvie in this issue.",
  journal   = "Radiology",
  publisher = "Radiological Society of North America (RSNA)",
  volume    =  290,
  number    =  2,
  pages     = "456--464",
  month     =  feb,
  year      =  2019,
  language  = "en"
}

@article{multimodal_multiscale_pet,
  doi = {10.1038/s41598-018-22871-z},
  url = {https://doi.org/10.1038/s41598-018-22871-z},
  year = {2018},
  month = apr,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {8},
  number = {1},
  author = {Donghuan Lu and   and Karteek Popuri and Gavin Weiguang Ding and Rakesh Balachandar and Mirza Faisal Beg},
  title = {{Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer's Disease using structural {MR} and {FDG}-{PET} images}},
  journal = {Scientific Reports}
}

@incollection{early_diagnosis_pet,
  doi = {10.1007/978-3-030-02698-1_53},
  url = {https://doi.org/10.1007/978-3-030-02698-1_53},
  year = {2018},
  publisher = {Springer International Publishing},
  pages = {614--622},
  author = {Chuanchuan Zheng and Yong Xia and Yuanyuan Chen and Xiaoxia Yin and Yanchun Zhang},
  title = {{Early Diagnosis of Alzheimer's Disease by Ensemble Deep Learning Using {FDG}-{PET}}},
  booktitle = {Lecture Notes in Computer Science}
}

@article{tau_pet,
  doi = {10.1186/s12859-020-03848-0},
  url = {https://doi.org/10.1186/s12859-020-03848-0},
  year = {2020},
  month = dec,
  publisher = {Springer Science and Business Media {LLC}},
  volume = {21},
  number = {S21},
  author = {Taeho Jo and   and Kwangsik Nho and Shannon L. Risacher and Andrew J. Saykin},
  title = {{Deep learning detection of informative features in tau {PET} for Alzheimer's disease classification}},
  journal = {{BMC} Bioinformatics}
}

@ARTICLE{followmyeye,
  author={Wang, Sheng and Ouyang, Xi and Liu, Tianming and Wang, Qian and Shen, Dinggang},
  journal={IEEE Transactions on Medical Imaging}, 
  title={{Follow My Eye: Using Gaze to Supervise Computer-Aided Diagnosis}}, 
  year={2022},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TMI.2022.3146973}}
  
  
@misc{ViT,
  doi = {10.48550/ARXIV.2205.12466},
  
  url = {https://arxiv.org/abs/2205.12466},
  
  author = {Ma, Chong and Zhao, Lin and Chen, Yuzhong and Zhang, Lu and Xiao, Zhenxiang and Dai, Haixing and Liu, David and Wu, Zihao and Liu, Zhengliang and Wang, Sheng and Gao, Jiaxing and Li, Changhe and Jiang, Xi and Zhang, Tuo and Wang, Qian and Shen, Dinggang and Zhu, Dajiang and Liu, Tianming},
  
  keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences, J.6; I.5.2; I.4.9, 68T07, 68T10},
  
  title = {{Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning}},
  
  publisher = {arXiv},
  
  year = {2022},
  
  copyright = {arXiv.org perpetual, non-exclusive license}
}

@inproceedings{Transformers,
title={{An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale}},
author={Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=YicbFdNTTy}
}

@INPROCEEDINGS{cam,
  author={Zhou, Bolei and Khosla, Aditya and Lapedriza, Agata and Oliva, Aude and Torralba, Antonio},
  booktitle={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, 
  title={{Learning Deep Features for Discriminative Localization}}, 
  year={2016},
  volume={},
  number={},
  pages={2921-2929},
  doi={10.1109/CVPR.2016.319}}
  
  
@ARTICLE{attention_nlp,
  author={Galassi, Andrea and Lippi, Marco and Torroni, Paolo},
  journal={IEEE Transactions on Neural Networks and Learning Systems}, 
  title={{Attention in Natural Language Processing}}, 
  year={2021},
  volume={32},
  number={10},
  pages={4291-4308},
  doi={10.1109/TNNLS.2020.3019893}}
  
  
@article{survey_AD_DL_2022,
title = {{Deep learning for Alzheimer's disease diagnosis: A survey}},
journal = {Artificial Intelligence in Medicine},
volume = {130},
pages = {102332},
year = {2022},
issn = {0933-3657},
doi = {https://doi.org/10.1016/j.artmed.2022.102332},
url = {https://www.sciencedirect.com/science/article/pii/S0933365722000975},
author = {M. Khojaste-Sarakhsi and Seyedhamidreza Shahabi Haghighi and S.M.T. Fatemi Ghomi and Elena Marchiori},
keywords = {Alzheimer's disease diagnosis, Deep learning, Deep neural networks, Convolutional neural networks, Recurrent neural networks, Generative networks},
}

@article{Zheng_2022,
	doi = {10.1088/1742-6596/2171/1/012068},
	url = {https://doi.org/10.1088/1742-6596/2171/1/012068},
	year = 2022,
	month = {jan},
	publisher = {{IOP} Publishing},
	volume = {2171},
	number = {1},
	pages = {012068},
	author = {Menghua Zheng and Jiayu Xu and Yinjie Shen and Chunwei Tian and Jian Li and Lunke Fei and Ming Zong and Xiaoyang Liu},
	title = {{Attention-based {CNNs} for Image Classification: A Survey}},
	journal = {Journal of Physics: Conference Series},
	abstract = {Deep learning techniques as well as CNNs can learn power context information, they have been widely applied in image recognition. However, deep CNNs may reply on large width and large depth, which may increase computational costs. Attention mechanism fused into CNNs can address this problem. In this paper, we summary an attention mechanism acts a CNN for image classification. Firstly, the survey shows the development of CNNs for image classification. Then, we illustrate basis of CNNs and attention mechanisms for image classification. Next, we give the main architecture of CNNs with attentions, public and our collected datasets, experimental results in image classification. Finally, we point out potential research points, challenges attention-based for image classification and summary the whole paper.}
}

@article{radiologists_accuracy,
    author = {Klöppel, Stefan and Stonnington, Cynthia M. and Barnes, Josephine and Chen, Frederick and Chu, Carlton and Good, Catriona D. and Mader, Irina and Mitchell, L. Anne and Patel, Ameet C. and Roberts, Catherine C. and Fox, Nick C. and Jack, Clifford R., Jr and Ashburner, John and Frackowiak, Richard S. J.},
    title = {{Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method}},
    journal = {Brain},
    volume = {131},
    number = {11},
    pages = {2969-2974},
    year = {2008},
    month = {10},
    issn = {0006-8950},
    doi = {10.1093/brain/awn239},
    url = {https://doi.org/10.1093/brain/awn239},
    eprint = {https://academic.oup.com/brain/article-pdf/131/11/2969/1062108/awn239.pdf},
}


@inproceedings{Mitsuhara2021,
  title={{Embedding Human Knowledge in Deep Neural Network via Attention Map}},
  author={Masahiro Mitsuhara and Hiroshi Fukui and Yusuke Sakashita and Takanori Ogata and Tsubasa Hirakawa and Takayoshi Yamashita and Hironobu Fujiyoshi},
  booktitle={VISIGRAPP},
  year={2021}
}

@ARTICLE{Fang2019,
  author={Fang, Leyuan and Wang, Chong and Li, Shutao and Rabbani, Hossein and Chen, Xiangdong and Liu, Zhimin},
  journal={IEEE Transactions on Medical Imaging}, 
  title={{Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification}}, 
  year={2019},
  volume={38},
  number={8},
  pages={1959-1970},
  doi={10.1109/TMI.2019.2898414}}
  
  
@inproceedings{Cui2020,
author = {Cui, Hui and Xu, Yiyue and Li, Wanlong and Wang, Linlin and Duh, Henry},
title = {{Collaborative Learning of Cross-Channel Clinical Attention for Radiotherapy-Related Esophageal Fistula Prediction from CT}},
year = {2020},
isbn = {978-3-030-59709-2},
publisher = {Springer-Verlag},
address = {Berlin, Heidelberg},
url = {https://doi.org/10.1007/978-3-030-59710-8_21},
doi = {10.1007/978-3-030-59710-8_21},
booktitle = {Medical Image Computing and Computer Assisted Intervention – MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part I},
pages = {212–220},
numpages = {9},
keywords = {CT, Cross channel attention, Esophageal fistula prediction},
location = {Lima, Peru}
}


@article{Xiao-Zheng_Xie,
author = {Xiao-Zheng Xie and Jian-Wei Niu and Xue-Feng Liu and Qing-Feng Li and Yong Wang and Jie Han and Shaojie Tang},
title = {{DG-CNN: Introducing Margin Information into Convolutional Neural Networks for Breast Cancer Diagnosis in Ultrasound Images}},
publisher = {Journal of Computer Science and Technology},
year = {2022},
journal = {Journal of Computer Science and Technology},
volume = {37},
number = {2},
eid = {277},
numpages = {17},
pages = {277},
url = {https://jcst.ict.ac.cn/EN/abstract/article_2863.shtml},
doi = {10.1007/s11390-020-0192-0}
}    


@ARTICLE{Detection_Melanoma,
  author={Zhang, Boyan and Wang, Zhiyong and Gao, Junbin and Rutjes, Chantal and Nufer, Kaitlin and Tao, Dacheng and Feng, David Dagan and Menzies, Scott W.},
  journal={IEEE Transactions on Medical Imaging}, 
  title={{Short-Term Lesion Change Detection for Melanoma Screening With Novel Siamese Neural Network}}, 
  year={2021},
  volume={40},
  number={3},
  pages={840-851},
  doi={10.1109/TMI.2020.3037761}}