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Accounting for Random Regressors: A Unified Approach to Multi-modality Imaging
[chapter]
2011
Lecture Notes in Computer Science
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Herein, we describe a unified regression and inference approach using the design matrix paradigm which accounts for both random and non-random imaging regressors. ...
Current statistical methods assume that the regressors are non-random. ...
This work described herein has not been submitted elsewhere for publication. ...
doi:10.1007/978-3-642-24446-9_1
pmid:25346952
pmcid:PMC4208720
fatcat:ben2qe7dvbhajgtp6teso4qhgu
Biological parametric mapping accounting for random regressors with regression calibration and model II regression
2012
NeuroImage
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These methods use the design matrix paradigm and account for both random and non-random imaging regressors. ...
Herein, we discuss two unified regression and inference approaches, model II regression and regression calibration, for use in massively univariate inference with imaging data. ...
Regression analysis accounting for errors in regressors would greatly improve the credibility of the BPM model by reasonably considering the randomness of the imaging modality in both the regressors and ...
doi:10.1016/j.neuroimage.2012.05.020
pmid:22609453
pmcid:PMC3408815
fatcat:hztpvvhvcvdlxlv4hjvvviibem
Canonical feature selection for joint regression and multi-class identification in Alzheimer's disease diagnosis
2015
Brain Imaging and Behavior
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We then perform the sparse multi-task learning for discriminative feature selection by using the canonical features as regressors and penalizing a loss function with a canonical regularizer. ...
In this paper, we propose a novel method to transform the original features from different modalities to a common space, where the transformed features become comparable and easy to find their relation ...
There are two approaches for multi-class classification (Suk and Lee 2013; Zhang and Shen 2012) , such as one-against-rest and oneagainst-one. ...
doi:10.1007/s11682-015-9430-4
pmid:26254746
pmcid:PMC4747862
fatcat:ce5aejjgpfhahc5gmf7ouvoxne
"You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
[article]
2020
arXiv
pre-print
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We discuss the implications of the proposed approach for three benchmark images datasets and also the challenges in using the approach for text modality. ...
We propose a model encoder approach to learn a fixed length representation of deep learning architectures along with its hyperparameters, in an unsupervised fashion. ...
the image modality: (i) GIST [22] (ii) DAISY [30] (iii) Local Binary Pattern (LBP) [35] . ...
arXiv:1911.11433v2
fatcat:w4ki22d2hzhhvf5fy3e4wsqiye
M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients
[article]
2020
arXiv
pre-print
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A multi-modal shared network is built to fuse these features using a bilinear pooling model, exploiting their correlations to provide complementary information. ...
To address the above issues, we propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net). ...
[9] conducted OS prediction by training an ensemble of a random forest regressor. Nie et al. ...
arXiv:2006.10135v2
fatcat:blzhuroxyzgo7crgim4nmcgzse
Cascade Graph Neural Networks for RGB-D Salient Object Detection
[article]
2020
arXiv
pre-print
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the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection. ...
images is how to fully leverage the two complementary data sources. ...
Classical approaches extract handcrafted features from the input RGB-D data and perform cross-modality feature fusion by various strategies, such as random forest regressor [56] and minimum barrier distance ...
arXiv:2008.03087v1
fatcat:nrjn4fkk65bynf3yyzfjn4wimy
Deep Head Pose: Gaze-Direction Estimation in Multimodal Video
2015
IEEE transactions on multimedia
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In this paper we present a convolutional neural network (CNN)-based model for human head pose estimation in low-resolution multi-modal RGB-D data. ...
We further fine-tune a regressor based on the learned deep classifier. Next we combine the two models (classification and regression) to estimate approximate regression confidence. ...
They would also like to thank Microsoft for providing access to the Kinect developer program. ...
doi:10.1109/tmm.2015.2482819
fatcat:qrz7fhtnynbjbfwt6eqlr4enw4
Revealing the mechanisms behind novel auditory stimuli discrimination: An evaluation of silent functional MRI using looping star
2021
Human Brain Mapping
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Looping Star is a near-silent, multi-echo, 3D functional magnetic resonance imaging (fMRI) technique. ...
We aimed to demonstrate, for the first time, that multi-echo Looping Star has sufficient sensitivity to the BOLD response, compared to that of GRE-EPI, during a well-established event-related auditory ...
Scale of ICC z-score maps adjusted to account for functional sensitivity differences between modalities et al. ...
doi:10.1002/hbm.25407
pmid:33729637
pmcid:PMC8127154
fatcat:gbrfmb7xivflbc2v4sppnofata
Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting
2018
The Plant Journal
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We demonstrate that our architecture can count leaves from multi-modal 2D images, such as visible light, fluorescence and near-infrared. ...
Our network design is flexible, allowing for inputs to be added or removed to accommodate new modalities. ...
ACKNOWLEDGEMENTS We thank Nvidia Corp. for providing the GPU used for this paper. We also thank Andrei Dobrescu for his valuable help. ...
doi:10.1111/tpj.14064
pmid:30101442
fatcat:lr24zpm6arhvhdh76tmol5rjpu
Highly parallel steered mixture-of-experts rendering at pixel-level for image and light field data
2018
Journal of Real-Time Image Processing
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In particular, it has strong potential for coding and streaming higher dimensional image modalities that are necessary to leverage full translational and rotational freedom (6 Degrees-of-Freedom) in virtual ...
A novel image approximation framework called Steered Mixture-of-Experts (SMoE) was recently presented. ...
The joint probability function of the coordinate space X and color space Y is modeled as a multi-modal, multi-variate Gaussian Mixture Model. ...
doi:10.1007/s11554-018-0843-3
fatcat:pcubeilcizeu5ezx52wnv2udvi
Ensemble CCA for Continuous Emotion Prediction
2014
Proceedings of the 4th International Workshop on Audio/Visual Emotion Challenge - AVEC '14
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Combining both representations in a CCA ensemble approach, on the challenge test set we reach an average Pearson's Correlation Coefficient (PCC) of 0.3932, outperforming the ASC test set baseline PCC of ...
We obtain an ensemble of regional linear regressors via CCA and MPGI. ...
The authors thank the anonymous reviewers for beneficial suggestions. The first author is a member of Faculty Member Training Program (ÖYP) supervised by the Turkish Higher Education Council (YÖK). ...
doi:10.1145/2661806.2661814
dblp:conf/mm/KayaCS14
fatcat:rahuwjxrevfzbexzeef5a4wsvy
Review of Disentanglement Approaches for Medical Applications – Towards Solving the Gordian Knot of Generative Models in Healthcare
[article]
2022
arXiv
pre-print
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Deep neural networks are commonly used for medical purposes such as image generation, segmentation, or classification. ...
Encouraging the latent representation of a generative model to be disentangled offers new perspectives of control and interpretability. ...
Multi-Modal Brain Analysis
Results from
Synthetic MRI Modality Generation For Magnetic Resonance (MR) images, different contrast acquisitions show different aspects, important for diagnosis. ...
arXiv:2203.11132v1
fatcat:fxrniu6dtjcz5cumwientkqh7i
Seeing Beyond Appearance - Mapping Real Images into Geometrical Domains for Unsupervised CAD-based Recognition
[article]
2018
arXiv
pre-print
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a more refined mapping for unseen color images. ...
Applying our approach to object recognition from texture-less CAD data, we present a custom generative network which fully utilizes the purely geometrical information to learn robust features and achieve ...
[27] use a unified Deep Convolutional Neural Fields (DCNF) framework based on the combination of a CNN and conditional random field (CRF) to regress depth from monocular color images of various scenes ...
arXiv:1810.04158v1
fatcat:vhywhkfbjnbsndpjjpabf537b4
Self-supervised learning methods and applications in medical imaging analysis: A survey
[article]
2021
arXiv
pre-print
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This article reviews the state-of-the-art research directions in self-supervised learning approaches for image data with concentration on their applications in the field of medical imaging analysis. ...
The article covers a set of the most recent self-supervised learning methods from the computer vision field as they are applicable to the medical imaging analysis and categorize them as predictive, generative ...
[2020a] proposed multi-modal reconstruction task as a self-supervised approach for retinal anatomy learning. ...
arXiv:2109.08685v2
fatcat:iu2zanqqrnaflawcxndb6xszgu
Audio-Visual Perception of 3D Cinematography: An fMRI Study Using Condition-Based and Computation-Based Analyses
2013
PLoS ONE
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Here, we exploited both approaches to investigate the neural correlates of complex visual and auditory spatial signals in cinematography. ...
The complexity of the surround sounds was associated with activity in specific sub-regions of S/MTG, even after accounting for changes of sound intensity. ...
Specifically, we considered the sound intensity contrast extracted using the same multi-scale approach adopted for the visual modality (see also [55] ). ...
doi:10.1371/journal.pone.0076003
pmid:24194828
pmcid:PMC3806767
fatcat:bif2ixoe7jafxg77hynebpiuw4
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