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Regularized siamese neural network for unsupervised outlier detection on brain multiparametric magnetic resonance imaging: application to epilepsy lesion …
In this study, we propose a novel anomaly detection model targeting subtle brain lesions in
multiparametric MRI. To compensate for the lack of annotated data adequately sampling the …
multiparametric MRI. To compensate for the lack of annotated data adequately sampling the …
Matrix-regularized one-class multiple kernel learning for unseen face presentation attack detection
SR Arashloo - IEEE Transactions on Information Forensics and …, 2021 - ieeexplore.ieee.org
The functionality of face biometric systems is severely challenged by presentation attacks
(PA's), and especially those attacks that have not been available during the training phase of …
(PA's), and especially those attacks that have not been available during the training phase of …
One-class classification using ℓp-norm multiple kernel fisher null approach
SR Arashloo - IEEE Transactions on Image Processing, 2023 - ieeexplore.ieee.org
We address the one-class classification (OCC) problem and advocate a one-class MKL
(multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null …
(multiple kernel learning) approach for this purpose. To this aim, based on the Fisher null …
Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI
Z Alaverdyan - 2019 - hal.science
This work represents one attempt to develop a computer aided diagnosis system for
epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR …
epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR …
-Norm Multiple Kernel One-Class Fisher Null-Space
SR Arashloo - arxiv preprint arxiv:2008.08642, 2020 - arxiv.org
The paper addresses the multiple kernel learning (MKL) problem for one-class classification
(OCC). For this purpose, based on the Fisher null-space one-class classification principle …
(OCC). For this purpose, based on the Fisher null-space one-class classification principle …
Sparse Multinomial Logistic Regression Algorithm Based on Centered Alignment Multiple Kernels Learning
D LEI, J TANG, Z LI, Y WU - 电子与信息学报, 2020 - jeit.ac.cn
As a generalized linear model, Sparse Multinomial Logistic Regression (SMLR) is widely
used in various multi-class task scenarios. SMLR introduces Laplace priori into Multinomial …
used in various multi-class task scenarios. SMLR introduces Laplace priori into Multinomial …
[PDF][PDF] 基于中心对齐多核学**的稀疏多元逻辑回归算法
雷大江, 唐建烊, **智星, 吴渝 - 电 子 与 信 息 学 报, 2020 - jeit.ac.cn
稀疏多元逻辑回归(SMLR) 作为一种广义的线性模型被广泛地应用于各种多分类任务场景中.
SMLR 通过将拉普拉斯先验引入多元逻辑回归(MLR) 中使其解具有稀疏性 …
SMLR 通过将拉普拉斯先验引入多元逻辑回归(MLR) 中使其解具有稀疏性 …
[PDF][PDF] Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI
JM Cardoso - 2019 - researchgate.net
Epilepsy affects around 50 million people worldwide, a third of those diagnosed with
medically refractory epilepsy where seizures cannot be controlled by pharmacotherapy. For …
medically refractory epilepsy where seizures cannot be controlled by pharmacotherapy. For …