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Improving 3D convolutional neural network comprehensibility via interactive visualization of relevance maps: Evaluation in Alzheimer's disease [article]

Martin Dyrba, Moritz Hanzig, Slawek Altenstein, Sebastian Bader, Tommaso Ballarini, Frederic Brosseron, Katharina Buerger, Daniel Cantré, Peter Dechent, Laura Dobisch, Emrah Düzel, Michael Ewers (+28 others)
2021 accepted
To improve model comprehensibility, we implemented an interactive visualization of 3D CNN relevance maps.  ...  Although convolutional neural networks (CNN) achieve high diagnostic accuracy for detecting Alzheimer's disease (AD) dementia based on magnetic resonance imaging (MRI) scans, they are not yet applied in  ...  Acknowledgements The data samples were provided by the DELCODE study group of the Clinical Research Unit of the German Center for Neurodegenerative Diseases (DZNE). Details  ... 
doi:10.1186/s13195-021-00924-2 pmid:34814936 pmcid:PMC8611898 arXiv:2012.10294v5 fatcat:e3svvu3p7zcrre2lnijzv3etpq

A parameter-efficient deep learning approach to predict conversion from mild cognitive impairment to Alzheimer's disease [article]

Simeon Spasov, Luca Passamonti, Andrea Duggento, Pietro Lio, Nicola Toschi
2018 bioRxiv   pre-print
both MCI to AD conversion, and AD vs healthy classification which facilitates the relevant feature extraction for prognostication; 2) the neural network classifier employs relatively few parameters compared  ...  The convolutional framework is potentially applicable to any 3D image dataset and gives the flexibility to design a computer-aided diagnosis system targeting the prediction of any medical condition utilizing  ...  The set of K feature maps extracted from the input x defines a single layer ℓ = [1, …, L] in our convolutional neural network. Thus, the k th feature map at layer ℓ is denoted as hk ℓ .  ... 
doi:10.1101/383687 fatcat:n5whvglxj5ajpptt5i7o2cvzeq

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 Sensors  
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interacting nodes connected by edges whose weights can be  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s21144758 fatcat:jytyt4u2pjgvhnhcto3vcvd3a4

Deep Learning in the Biomedical Applications: Recent and Future Status

Ryad Zemouri, Noureddine Zerhouni, Daniel Racoceanu
2019 Applied Sciences  
Deep neural networks represent, nowadays, the most effective machine learning technology in biomedical domain.  ...  ), medical imaging (study of the human organs by creating visual representations), BBMI (study of the brain and body machine interface) and public and medical health management (PmHM).  ...  Convolutional neural networks The convolutional neural networks (CNNs) were inspired by the neurobiological model of the visual cortex, where the cells are sensitive to small regions of the visual field  ... 
doi:10.3390/app9081526 fatcat:srjvngtufbhstfcvn4mvhmrdve

Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future [article]

David Ahmedt-Aristizabal, Mohammad Ali Armin, Simon Denman, Clinton Fookes, Lars Petersson
2021 arXiv   pre-print
As such, graph neural networks have attracted significant attention by exploiting implicit information that resides in a biological system, with interactive nodes connected by edges whose weights can be  ...  In this survey, we thoroughly review the different types of graph architectures and their applications in healthcare.  ...  Following the success of convolutional neural networks, Bruna et al.  ... 
arXiv:2105.13137v1 fatcat:gm7d2ziagba7bj3g34u4t3k43y

Alzheimer's Diseases Detection by Using Deep Learning Algorithms: A Mini-Review

Suhad Al-Shoukry, Taha H. Rassem, Nasrin M. Makbol
2020 IEEE Access  
The accurate diagnosis of Alzheimer's disease (AD) plays an important role in patient treatment, especially at the disease's early stages, because risk awareness allows the patients to undergo preventive  ...  Here, we briefly review some of the important literature on AD and explore how DL can help researchers diagnose the disease at its early stages.  ...  architecture can be divided into Convolutional Neural Network (CNN) and RNN.  ... 
doi:10.1109/access.2020.2989396 fatcat:stcpfqcan5cerltivdu3qiyiaq

Early Detection of Alzheimer's Disease Using Magnetic Resonance Imaging: A Novel Approach Combining Convolutional Neural Networks and Ensemble Learning

Dan Pan, An Zeng, Longfei Jia, Yin Huang, Tory Frizzell, Xiaowei Song
2020 Frontiers in Neuroscience  
Among several deep-learning techniques that have been applied to assessing structural brain changes on magnetic resonance imaging (MRI), convolutional neural network (CNN) has gained popularity due to  ...  Early detection is critical for effective management of Alzheimer's disease (AD) and screening for mild cognitive impairment (MCI) is common practice.  ...  This study was supported by NSF of China (Grant Nos. 61976058 and 61772143), Science and Technology  ... 
doi:10.3389/fnins.2020.00259 pmid:32477040 pmcid:PMC7238823 fatcat:5pueqzg6hfftbctr642clomun4

A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAI [article]

Erico Tjoa, Cuntai Guan
2020 arXiv   pre-print
of deep learning.  ...  The different categories show different dimensions in interpretability research, from approaches that provide "obviously" interpretable information to the studies of complex patterns.  ...  Note: an example of feature map is the output of a convolutional filter in a Convolutional Neural Network (CNN).  ... 
arXiv:1907.07374v5 fatcat:ssup2eanlvertbcztuovdakykq

3D Mapping of Neurofibrillary Tangle Burden in the Human Medial Temporal Lobe [article]

Paul A Yushkevich, Mónica Muñoz López, Maria Mercedes Iñiguez de Onzoño Martin, Ranjit Ittyerah, Sydney Lim, Sadhana Ravikumar, Madigan L. Bedard, Stephen Pickup, Weixia Liu, Jiancong Wang, Ling Yu Hung, Jade Lasserve (+28 others)
2021 bioRxiv   pre-print
Tau protein neurofibrillary tangles (NFT) are closely linked to neuronal/synaptic loss and cognitive decline in Alzheimer's disease (AD) and related dementias.  ...  To address this limitation, ex vivo MRI and dense serial histological imaging in 18 human medial temporal lobe (MTL) specimens were used to construct 3D quantitative maps of NFT burden in the MTL at individual  ...  in Alzheimer's disease.  ... 
doi:10.1101/2021.01.15.421909 fatcat:s3nobgjpxreancsjqx7hc7nzwy

Attention, please! A survey of Neural Attention Models in Deep Learning [article]

Alana de Santana Correia, Esther Luna Colombini
2021 arXiv   pre-print
This survey provides a comprehensive overview and analysis of developments in neural attention models.  ...  By critically analyzing 650 works, we describe the primary uses of attention in convolutional, recurrent networks and generative models, identifying common subgroups of uses and applications.  ...  The neural map [529] maintains an internal memory in the agent controlled via attention mechanisms.  ... 
arXiv:2103.16775v1 fatcat:lwkw42lrircorkymykpgdmlbwq

Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review [article]

Felipe Giuste, Wenqi Shi, Yuanda Zhu, Tarun Naren, Monica Isgut, Ying Sha, Li Tong, Mitali Gupte, May D. Wang
2021 arXiv   pre-print
We find that successful use of XAI can improve model performance, instill trust in the end-user, and provide the value needed to affect user decision-making.  ...  Evaluation of XAI results is also discussed as an important step to maximize the value of AI-based clinical decision support systems.  ...  Siva Bhavani from Emory University for his insights on leveraging artificial intelligence in clinical practice. We would like to thank Mr.  ... 
arXiv:2112.12705v2 fatcat:pji2saeikbeq7phmygphbomm5e

Application of deep learning in detecting neurological disorders from magnetic resonance images: a survey on the detection of Alzheimer's disease, Parkinson's disease and schizophrenia

Manan Binth Taj Noor, Nusrat Zerin Zenia, M Shamim Kaiser, Shamim Al Mamun, Mufti Mahmud
2020 Brain Informatics  
The comparative performance analysis of various DL architectures across different disorders and imaging modalities suggests that the Convolutional Neural Network outperforms other methods in detecting  ...  This article critically examines and compares performances of the existing deep learning (DL)-based methods to detect neurological disorders—focusing on Alzheimer's disease, Parkinson's disease and schizophrenia—from  ...  Acknowledgements The authors would like to thank the members of the acslab (http://www.acsla b.info/) for valuable discussions.  ... 
doi:10.1186/s40708-020-00112-2 pmid:33034769 pmcid:PMC7547060 fatcat:vvnq3knq4rgfrcwov3yr2s3mk4

DeepHealth: Review and challenges of artificial intelligence in health informatics [article]

Gloria Hyunjung Kwak, Pan Hui
2020 arXiv   pre-print
This article presents a comprehensive review of research applying artificial intelligence in health informatics, focusing on the last seven years in the fields of medical imaging, electronic health records  ...  infectious disease outbreaks with high accuracy.  ...  Convolutional Neural Networks Convolutional neural network (CNN) is an algorithm inspired by biological processing of the animal visual cortex [23, 64, 65] .  ... 
arXiv:1909.00384v2 fatcat:sy7pm2c2uvdd3pal2russn4xri

Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: a review [article]

Fan Zhang, Alessandro Daducci, Yong He, Simona Schiavi, Caio Seguin, Robert Smith, Chun-Hung Yeh, Tengda Zhao, Lauren J. O'Donnell
2021 arXiv   pre-print
In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease.  ...  Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the brain's white matter connections at macro scale.  ...  CHY is grateful to the Ministry of Science and Technology of Taiwan (MOST 109-2222-E-182-001-MY3) for the support.  ... 
arXiv:2104.11644v1 fatcat:l3ixpcwu7jb7zcqo2sm5pswmja

Medical Deep Learning – A systematic Meta-Review [article]

Jan Egger, Christina Gsaxner, Antonio Pepe, Jianning Li
2020 arXiv   pre-print
Moreover, deep learning delivered state-of-the-art results in tasks like autonomous driving, outclassing previous attempts.  ...  They focus, in general, on specific medical scenarios, like the analysis of medical images containing specific pathologies.  ...  They give an overview of main network architectures, with a special attention to convolutional neural networks.  ... 
arXiv:2010.14881v4 fatcat:56nrzawncnaopcpuzlzac5ceoy
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