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Unsupervised Lesion Detection via Image Restoration with a Normative Prior [article]

Xiaoran Chen, Suhang You, Kerem Can Tezcan, Ender Konukoglu
2020 arXiv   pre-print
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

Rini Smita Thakur, Shubhojeet Chatterjee, Ram Narayan Yadav, Lalita Gupta
2021 IEEE Access  
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'

Daniel Biediger, Christophe Collet, Jean‐Paul Armspach
2014 Journal of Computational Surgery  
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  
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

A. Mayer, H. Greenspan
2009 IEEE Transactions on Medical Imaging  
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]

Hyunseok Seo, Masoud Badiei Khuzani, Varun Vasudevan, Charles Huang, Hongyi Ren, Ruoxiu Xiao, Xiao Jia, Lei Xing
2019 Medical Physics (Lancaster)   pre-print
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

Muhammad Moazzam Jawaid, Atif Riaz, Ronak Rajani, Constantino Carlos Reyes-Aldasoro, Greg Slabaugh
2017 Computers in Biology and Medicine  
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]

Sebastian Houben, Stephanie Abrecht, Maram Akila, Andreas Bär, Felix Brockherde, Patrick Feifel, Tim Fingscheidt, Sujan Sai Gannamaneni, Seyed Eghbal Ghobadi, Ahmed Hammam, Anselm Haselhoff, Felix Hauser (+29 others)
2021 arXiv   pre-print
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

Oula Puonti, Juan Eugenio Iglesias, Koen Van Leemput
2016 NeuroImage  
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]

Nicolas Jouvin , Guillaume Bataillon Laboratoire SAMM EA 4543,
2019 arXiv   pre-print
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]

Greg Wayne, Chia-Chun Hung, David Amos, Mehdi Mirza, Arun Ahuja, Agnieszka Grabska-Barwinska, Jack Rae, Piotr Mirowski, Joel Z. Leibo, Adam Santoro, Mevlana Gemici, Malcolm Reynolds, Tim Harley (+11 others)
2018 arXiv   pre-print
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

Ila R. Fiete, Michale S. Fee, H. Sebastian Seung
2007 Journal of Neurophysiology  
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  
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

P. Dayan, N. D. Daw
2008 Cognitive, Affective, & Behavioral Neuroscience  
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


1988 The Hastings center report  
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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