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Unsupervised Lesion Detection via Image Restoration with a Normative Prior
[article]
2020
arXiv
pre-print
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Unsupervised lesion detection is a challenging problem that requires accurately estimating normative distributions of healthy anatomy and detecting lesions as outliers without training examples. ...
In this work, we approach unsupervised lesion detection as an image restoration problem and propose a probabilistic model that uses a network-based prior as the normative distribution and detect lesions ...
Acknowledgments We thank Swiss National Science Foundation (SNSF) and Platform for Advanced Scientific Computing (PASC) for funding this project (project no. 205321 173016), as well as Nvidia for GPU donations ...
arXiv:2005.00031v1
fatcat:6n5pybxzd5d5bkxwcu7thr5mvu
Image De-noising with Machine Learning: A Review
2021
IEEE Access
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The best de-noising results for different noise type is discussed along with future prospects. Among various Gaussian noise de-noisers, GCBD, BRDNet and PReLU network prove to be promising. ...
of noises like Gaussian, Impulse, Poisson, Mixed and Real-World noises. ...
The energy minimization model with the weighted 2 − 0 norm is being used for mixed noise removal such as Gaussian-Gaussian mixture, impulse noise, and Gaussian-impulse noise. ...
doi:10.1109/access.2021.3092425
fatcat:xirq6soukzchvaeiugcpgxnlqi
Multiple sclerosis lesion detection with local multimodal Markovian analysis and cellular automata 'GrowCut'
2014
Journal of Computational Surgery
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This approach better captures the local lesion properties and produces encouraging results, with a general improvement in the detection rate of lesions. ...
Just as a physician might look more closely at a lesion, it considers the local neighborhood around a lesion detection. ...
Acknowledgements We would like to thank the European Union and program Atlantis for funding mobility between the University of Houston (Computer Sciences Dept), TX, USA and the University of Strasbourg ...
doi:10.1186/2194-3990-1-3
fatcat:25asym7jtbfydbzrmoctnff2uq
Scrutiny of Breast Cancer Detection Techniques of Deeplearning and Machine Learning
2019
International journal of recent technology and engineering
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The point of this paper is to audit existing ways to deal with the segmentation of masses and automated detection in mammographic pictures, underlining the key-focuses and primary contrasts among the utilized ...
network for classification of mammography images . ...
in the medical-imaging area for the detection of breast malignancy. ...
doi:10.35940/ijrte.b1034.0982s1019
fatcat:rvnveqyirzh2hhzeaqxgxotahe
An Adaptive Mean-Shift Framework for MRI Brain Segmentation
2009
IEEE Transactions on Medical Imaging
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By its nonparametric nature, adaptive mean-shift can deal successfully with nonconvex clusters and produce convergence modes that are better candidates for intensity based classification than the initial ...
An automated scheme for magnetic resonance imaging (MRI) brain segmentation is proposed. ...
Here, adaptive gradient ascent is used to detect local maxima of data density in feature space. Data points are associated with local maxima, or modes, thereby defining the clusters. ...
doi:10.1109/tmi.2009.2013850
pmid:19211339
fatcat:xl4j5c4o7nhmrhom7zyoefa4gm
Machine Learning Techniques for Biomedical Image Segmentation: An Overview of Technical Aspects and Introduction to State-of-Art Applications
[article]
2019
Medical Physics (Lancaster)
pre-print
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In recent years, significant progress has been made in developing more accurate and efficient machine learning algorithms for segmentation of medical and natural images. ...
For example, in the case of lesion detection, the number of normal voxels is typically 500 times larger than that of lesion voxels. ...
Linear regression models are often fitted using minimization of the l -norm (ex., 2-norm minimization is the least square approach). ...
doi:10.1002/mp.13649
pmid:32418337
arXiv:1911.02521v1
fatcat:z6lbdtxxqzclthwu4mijo5ss3y
Framework for detection and localization of coronary non-calcified plaques in cardiac CTA using mean radial profiles
2017
Computers in Biology and Medicine
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In this work, we propose the use of mean radial profiles for the detection of non-calcified plaques in CTA imagery. ...
For occluded segments, we further propose a derivative-based method to localize the position and length of the plaque inside the segment. ...
Moreover, we used a non-linear radial basis Gaussian kernel for mapping data into higher space with sigma defined equal to 1. ...
doi:10.1016/j.compbiomed.2017.07.021
pmid:28797740
fatcat:lkvuhfxe2va7vj6nogjargfsp4
Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
[article]
2021
arXiv
pre-print
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These shortcomings are diverse and range from a lack of generalization over insufficient interpretability to problems with malicious inputs. ...
We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. ...
Acknowledgment The research leading to these results is funded by the German Federal Ministry for Economic Affairs and Energy within the project "KI Absicherung -Safe AI for Automated Driving". ...
arXiv:2104.14235v1
fatcat:f6sj3v2brza7thyzw7b7fkpo2m
Fast and sequence-adaptive whole-brain segmentation using parametric Bayesian modeling
2016
NeuroImage
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A desirable feature for such segmentation methods is to be robust against changes in acquisition platform and imaging protocol. ...
The proposed algorithm is also shown to be robust against small training datasets, and readily handles images with different MRI contrast as well as multi-contrast data. ...
and a Gaussian kernel for the intensities); with an optional Markov random field prior enforcing spatial consistency. ...
doi:10.1016/j.neuroimage.2016.09.011
pmid:27612647
pmcid:PMC8117726
fatcat:6ce3ytwqqrddfbg7zwr4z6h4ay
Clustering of count data through a mixture of multinomial PCA
[article]
2019
arXiv
pre-print
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An integrated classification likelihood criterion is derived for model selection, and a thorough study with numerical experiments is proposed to assess both the performance and robustness of the method ...
Finally, we illustrate the qualitative interest of the latter in a real-world application, for the clustering of anatomopathological medical reports, in partnership with expert practitioners from the Institut ...
These are almost exclusively used to search for cancerous lesion after the detection of microcalcifications in a breast mammography. ...
arXiv:1909.00721v1
fatcat:mdf4uh53lrgc5cikxfcdahacgu
Unsupervised Predictive Memory in a Goal-Directed Agent
[article]
2018
arXiv
pre-print
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Recently, progress has been made with artificial intelligence (AI) agents that learn to perform tasks from sensory input, even at a human level, by merging reinforcement learning (RL) algorithms with deep ...
MERLIN facilitates the solution of tasks in 3D virtual reality environments for which partial observability is severe and memories must be maintained over long durations. ...
, Raia Hadsell, Brian Zhang, Oriol Vinyals, and Hubert Soyer for discussions; Amir Sadik and Sarah York for environment testing; Stephen Gaffney and Helen King for organisational help. ...
arXiv:1803.10760v1
fatcat:frzec2ieuvahnftr7stem4lrma
Model of Birdsong Learning Based on Gradient Estimation by Dynamic Perturbation of Neural Conductances
2007
Journal of Neurophysiology
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driven by hypothetical "rules" depending on three signals: activation of HVC 3 RA synapses, activation of LMAN 3 RA synapses, and reinforcement from an internal critic that compares the bird's own song with ...
Fluctuating glutamatergic input to RA from LMAN generates behavioral variability for trial-and-error learning. ...
The inputs are assumed to be drawn from one of two distributions. 1) A Gaussian distribution with zero mean. ...
doi:10.1152/jn.01311.2006
pmid:17652414
fatcat:7fxjq2fjhrbjtbamauonkp2yhm
American Association of Neuropathologists, Inc. Abstracts of the 97th Annual Meeting June 10-13, 2021 St. Louis, Missouri
2021
Journal of Neuropathology and Experimental Neurology
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Such stem cell model of FTD could also be used as a cellular platform for high-throughput drug screening assays to identify potential therapeutic targets in FTD. ; 5 BioSkryb As humans age, cells undergo ...
age, including neurons, with consequences in neurodegenerative disease. ...
We developed an object detection model for tau lesions using the YOLO object detection algorithm. ...
doi:10.1093/jnen/nlab042
pmid:34087938
fatcat:rgowg3kxi5ghpbylpalr2rxkj4
Decision theory, reinforcement learning, and the brain
2008
Cognitive, Affective, & Behavioral Neuroscience
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Decision theory is a core competence for animals and humans acting and surviving in environments they only partially comprehend, gaining rewards and punishments for their troubles. ...
Here, we review a well known, coherent Bayesian approach to decision-making, showing how it unifies issues in Markovian decision problems, signal detection psychophysics, sequential sampling and optimal ...
We also thank the editors and anonymous reviewers for their help comments. ...
doi:10.3758/cabn.8.4.429
pmid:19033240
fatcat:6l37gqtenfg3xonzfdd3kuk3sy
CORPORATE CONTRIBUTORS
1988
The Hastings center report
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stimulus contrast with saturation for low contrasts. the prior is to first approximation Gaussian (in particular for lower speeds) with zero mean and constant standard deviation. ...
A B C D X Location Y Location Coefficient Stat Norm
High Level We consider statistical normalization in relation to three related frameworks: Gaussian scale mixture models (GSMs), divisive normalization ...
Some have argued that this demonstrates that understanding neural computation is not relevant for understanding cognitive function (see Fodor and Pylyshyn, 1988; Jackendoff, 2002) . ...
doi:10.1002/j.1552-146x.1988.tb03932.x
fatcat:bkotcyah2ngbfdk4kbx3xnf5v4
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