@article{thompson_diagnosis_2018,
	title = {Diagnosis of multiple sclerosis: 2017 revisions of the {McDonald} criteria},
	volume = {17},
	issn = {14744422},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1474442217304702},
	doi = {10.1016/S1474-4422(17)30470-2},
	shorttitle = {Diagnosis of multiple sclerosis},
	pages = {162--173},
	number = {2},
	journaltitle = {The Lancet Neurology},
	author = {Thompson, Alan J and Banwell, Brenda L and Barkhof, Frederik and Carroll, William M and Coetzee, Timothy and Comi, Giancarlo and Correale, Jorge and Fazekas, Franz and Filippi, Massimo and Freedman, Mark S and Fujihara, Kazuo and Galetta, Steven L and Hartung, Hans Peter and Kappos, Ludwig and Lublin, Fred D and Marrie, Ruth Ann and Miller, Aaron E and Miller, David H and Montalban, Xavier and Mowry, Ellen M and Sorensen, Per Soelberg and Tintoré, Mar and Traboulsee, Anthony L and Trojano, Maria and Uitdehaag, Bernard M J and Vukusic, Sandra and Waubant, Emmanuelle and Weinshenker, Brian G and Reingold, Stephen C and Cohen, Jeffrey A},
	urldate = {2024-07-13},
	date = {2018-02},
	langid = {english},
	}

@article{Tustison_N4ITK_2010, title={N4ITK: Improved N3 Bias Correction}, volume={29}, ISSN={1558-254X}, url={http://dx.doi.org/10.1109/TMI.2010.2046908}, DOI={10.1109/tmi.2010.2046908}, number={6}, journal={IEEE Transactions on Medical Imaging}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Tustison, Nicholas J and Avants, Brian B and Cook, Philip A and Yuanjie Zheng and Egan, Alexander and Yushkevich, Paul A and Gee, James C}, year={2010}, month=jun, pages={1310–1320} }

@article{Lundberg_SHAP_2017,
title = {A Unified Approach to Interpreting Model Predictions},
author = {Lundberg, Scott M and Lee, Su-In},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {4765--4774},
year = {2017},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions.pdf}
}
@article{shapley_values_1953,
  title = {A Value for n-Person Games},
  author = {Shapley, Lloyd S},
  booktitle = {Contributions to the Theory of Games II},
  editor = {Kuhn, Harold W. and Tucker, Albert W.},
  pages = {307--317},
  year = {1953},
  publisher = {Princeton University Press},
  address = {Princeton},
  doi = {doi:10.1515/9781400881970-018},
  isbn = {9781400881970},
}
@article{gaitan2019multiple,
  title={Multiple sclerosis misdiagnosis: a persistent problem to solve},
  author={Gait{\'a}n, Mar{\'\i}a I and Correale, Jorge},
  journal={Frontiers in Neurology},
  volume={10},
  pages={466},
  year={2019},
  publisher={Frontiers Media SA}
}

@article{Shrikumar_DeepLIFT_2017,
author = {Shrikumar, Avanti and Greenside, Peyton and Kundaje, Anshul},
title = {Learning important features through propagating activation differences},
year = {2017},
publisher = {JMLR.org},
abstract = {The purported "black box" nature of neural networks is a barrier to adoption in applications where interpretability is essential. Here we present DeepLIFT (Deep Learning Important FeaTures), a method for decomposing the output prediction of a neural network on a specific input by backpropagating the contributions of all neurons in the network to every feature of the input. DeepLIFT compares the activation of each neuron to its 'reference activation' and assigns contribution scores according to the difference. By optionally giving separate consideration to positive and negative contributions, DeepLIFT can also reveal dependencies which are missed by other approaches. Scores can be computed efficiently in a single backward pass. We apply DeepLIFT to models trained on MNIST and simulated genomic data, and show significant advantages over gradient-based methods. Video tutorial: http://goo.gl/qKb7pL, code: http://goo.gl/RM8jvH.},
booktitle = {Proceedings of the 34th International Conference on Machine Learning - Volume 70},
pages = {3145–3153},
numpages = {9},
location = {Sydney, NSW, Australia},
series = {ICML'17}
}

@article{waubant2019environmental,
  title={Environmental and genetic risk factors for MS: an integrated review},
  author={Waubant, Emmanuelle and Lucas, Robyn and Mowry, Ellen and Graves, Jennifer and Olsson, Tomas and Alfredsson, Lars and Langer-Gould, Annette},
  journal={Annals of clinical and translational neurology},
  volume={6},
  number={9},
  pages={1905--1922},
  year={2019},
  publisher={Wiley Online Library}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}

@article{solomon2016contemporary,
  title={The contemporary spectrum of multiple sclerosis misdiagnosis: a multicenter study},
  author={Solomon, Andrew J and Bourdette, Dennis N and Cross, Anne H and Applebee, Angela and Skidd, Philip M and Howard, Diantha B and Spain, Rebecca I and Cameron, Michelle H and Kim, Edward and Mass, Michele K and others},
  journal={Neurology},
  volume={87},
  number={13},
  pages={1393--1399},
  year={2016},
  publisher={AAN Enterprises}
}

@article{Snell_protonet_2017,
     author = {Snell, Jake and Swersky, Kevin and Zemel, Richard},
     booktitle = {Advances in Neural Information Processing Systems},
     editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
     pages = {},
     publisher = {Curran Associates, Inc.},
     title = {Prototypical Networks for Few-shot Learning},
     url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/cb8da6767461f2812ae4290eac7cbc42-Paper.pdf},
     volume = {30},
     year = {2017}
}

@article{Simonyan_VGG_2014,
  title={Very Deep Convolutional Networks for Large-Scale Image Recognition},
  author={Karen Simonyan and Andrew Zisserman},
  journal={CoRR},
  year={2014},
  volume={abs/1409.1556},
  url={https://api.semanticscholar.org/CorpusID:14124313}
}

@article{Chattopadhay_Grad-CAM++_2018,
  author={Chattopadhay, Aditya and Sarkar, Anirban and Howlader, Prantik and Balasubramanian, Vineeth N},
  booktitle={2018 IEEE Winter Conference on Applications of Computer Vision (WACV)}, 
  title={Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks}, 
  year={2018},
  volume={},
  number={},
  pages={839-847},
  keywords={Visualization;Heating systems;Neurons;Machine learning;Predictive models;Mathematical model},
  doi={10.1109/WACV.2018.00097}}

@article{Huang_CVPR_densenet_2017,
    author = {Huang, Gao and Liu, Zhuang and van der Maaten, Laurens and Weinberger, Kilian Q.},
    title = {Densely Connected Convolutional Networks},
    booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    month = {July},
    year = {2017}
}

@article{zhang_grad-cam_2021,
	title = {Grad-{CAM} helps interpret the deep learning models trained to classify multiple sclerosis types using clinical brain magnetic resonance imaging},
	volume = {353},
	issn = {01650270},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0165027021000339},
	doi = {10.1016/j.jneumeth.2021.109098},
	language = {en},
	urldate = {2023-11-02},
	journal = {Journal of Neuroscience Methods},
	author = {Zhang, Yunyan and Hong, Daphne and McClement, Daniel and Oladosu, Olayinka and Pridham, Glen and Slaney, Garth},
	month = apr,
	year = {2021},
	pages = {109098}
}

@article{huang_transformer-based_2022,
	title = {Transformer-{Based} {Deep}-{Learning} {Algorithm} for {Discriminating} {Demyelinating} {Diseases} of the {Central} {Nervous} {System} {With} {Neuroimaging}},
	volume = {13},
	issn = {1664-3224},
	url = {https://www.frontiersin.org/articles/10.3389/fimmu.2022.897959/full},
	doi = {10.3389/fimmu.2022.897959},
	abstract = {Background
              Differential diagnosis of demyelinating diseases of the central nervous system is a challenging task that is prone to errors and inconsistent reading, requiring expertise and additional examination approaches. Advancements in deep-learning-based image interpretations allow for prompt and automated analyses of conventional magnetic resonance imaging (MRI), which can be utilized in classifying multi-sequence MRI, and thus may help in subsequent treatment referral.
            
            
              Methods
              Imaging and clinical data from 290 patients diagnosed with demyelinating diseases from August 2013 to October 2021 were included for analysis, including 67 patients with multiple sclerosis (MS), 162 patients with aquaporin 4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD), and 61 patients with myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD). Considering the heterogeneous nature of lesion size and distribution in demyelinating diseases, multi-modal MRI of brain and/or spinal cord were utilized to build the deep-learning model. This novel transformer-based deep-learning model architecture was designed to be versatile in handling with multiple image sequences (coronal T2-weighted and sagittal T2-fluid attenuation inversion recovery) and scanning locations (brain and spinal cord) for differentiating among MS, NMOSD, and MOGAD. Model performances were evaluated using the area under the receiver operating curve (AUC) and the confusion matrices measurements. The classification accuracy between the fusion model and the neuroradiological raters was also compared.
            
            
              Results
              The fusion model that was trained with combined brain and spinal cord MRI achieved an overall improved performance, with the AUC of 0.933 (95\%CI: 0.848, 0.991), 0.942 (95\%CI: 0.879, 0.987) and 0.803 (95\%CI: 0.629, 0.949) for MS, AQP4+ NMOSD, and MOGAD, respectively. This exceeded the performance using the brain or spinal cord MRI alone for the identification of the AQP4+ NMOSD (AUC of 0.940, brain only and 0.689, spinal cord only) and MOGAD (0.782, brain only and 0.714, spinal cord only). In the multi-category classification, the fusion model had an accuracy of 81.4\%, which was significantly higher compared to rater 1 (64.4\%, p=0.04\&lt;0.05) and comparable to rater 2 (74.6\%, p=0.388).
            
            
              Conclusion
              The proposed novel transformer-based model showed desirable performance in the differentiation of MS, AQP4+ NMOSD, and MOGAD on brain and spinal cord MRI, which is comparable to that of neuroradiologists. Our model is thus applicable for interpretating conventional MRI in the differential diagnosis of demyelinating diseases with overlapping lesions.},
	language = {en},
	urldate = {2023-09-14},
	journal = {Frontiers in Immunology},
	author = {Huang, Chuxin and Chen, Weidao and Liu, Baiyun and Yu, Ruize and Chen, Xiqian and Tang, Fei and Liu, Jun and Lu, Wei},
	month = jun,
	year = {2022},
	pages = {897959}
}

@article{iglesias_robust_2011,
	title = {Robust brain extraction across datasets and comparison with publicly available methods},
	volume = {30},
	issn = {1558-254X},
	doi = {10.1109/TMI.2011.2138152},
	abstract = {Automatic whole-brain extraction from magnetic resonance images ({MRI}), also known as skull stripping, is a key component in most neuroimage pipelines. As the first element in the chain, its robustness is critical for the overall performance of the system. Many skull stripping methods have been proposed, but the problem is not considered to be completely solved yet. Many systems in the literature have good performance on certain datasets (mostly the datasets they were trained/tuned on), but fail to produce satisfactory results when the acquisition conditions or study populations are different. In this paper we introduce a robust, learning-based brain extraction system ({ROBEX}). The method combines a discriminative and a generative model to achieve the final result. The discriminative model is a Random Forest classifier trained to detect the brain boundary; the generative model is a point distribution model that ensures that the result is plausible. When a new image is presented to the system, the generative model is explored to find the contour with highest likelihood according to the discriminative model. Because the target shape is in general not perfectly represented by the generative model, the contour is refined using graph cuts to obtain the final segmentation. Both models were trained using 92 scans from a proprietary dataset but they achieve a high degree of robustness on a variety of other datasets. {ROBEX} was compared with six other popular, publicly available methods ({BET}, {BSE}, {FreeSurfer}, {AFNI}, {BridgeBurner}, and {GCUT}) on three publicly available datasets ({IBSR}, {LPBA}40, and {OASIS}, 137 scans in total) that include a wide range of acquisition hardware and a highly variable population (different age groups, healthy/diseased). The results show that {ROBEX} provides significantly improved performance measures for almost every method/dataset combination.},
	pages = {1617--1634},
	number = {9},
	journaltitle = {{IEEE} transactions on medical imaging},
	shortjournal = {{IEEE} Trans Med Imaging},
	author = {Iglesias, Juan Eugenio and Liu, Cheng-Yi and Thompson, Paul M. and Tu, Zhuowen},
	date = {2011-09},
	pmid = {21880566},
	keywords = {Adult, Aged, Algorithms, Brain, Computer Simulation, Database Management Systems, Databases, Factual, Discriminant Analysis, Electronic Data Processing, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Middle Aged, Models, Anatomic, Pattern Recognition, Automated, Reproducibility of Results, Sensitivity and Specificity, Skull},
}


@article{wang_multiple_2018,
	title = {Multiple {Sclerosis} {Identification} by 14-{Layer} {Convolutional} {Neural} {Network} {With} {Batch} {Normalization}, {Dropout}, and {Stochastic} {Pooling}},
	volume = {12},
	issn = {1662-453X},
	url = {https://www.frontiersin.org/article/10.3389/fnins.2018.00818/full},
	doi = {10.3389/fnins.2018.00818},
	abstract = {Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.
Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35\%, a speciﬁcity of 98.76 ± 0.58\%, and an accuracy of 98.77 ± 0.39\%.
Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including ﬁve traditional artiﬁcial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.},
	language = {en},
	urldate = {2023-10-10},
	journal = {Frontiers in Neuroscience},
	author = {Wang, Shui-Hua and Tang, Chaosheng and Sun, Junding and Yang, Jingyuan and Huang, Chenxi and Phillips, Preetha and Zhang, Yu-Dong},
	month = nov,
	year = {2018},
	pages = {818}
}