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The Evolution of Breast Cancer Detection: A Review of Imaging, Machine Learning, and Multimodal Strategies

Computational and Systems Oncology, Volume 6, Issue 1, December 2026.
ABSTRACT Breast cancer is still a serious problem in the world arena, where its early and prompt detection is the most important factor in improving patient prognosis and survival. The use of traditional diagnostic techniques, such as imaging (e.g., mammography and ultrasound) and subsequent histopathological examination, is the mainstay, which ...
Likhon Chandra Sarkar, Farida Siddiqi Prity, Farhad Hussain, Anup Talukder, Mirza Raquib, Sharmina Rahman, MD Jiabul Hoque   +6 more
wiley   +1 more source

Stochastic Galerkin and Monte Carlo Methods for Parabolic Problems: Numerical Performance of Variational Matrix‐Free Approximations

Proceedings in Applied Mathematics and Mechanics, Volume 26, Issue 4, December 2026.
ABSTRACT Stochastic Galerkin methods offer unexplored potential for the numerical simulation of parabolic problems with random variables, in particular if they are combined with variational discretizations of the space and time variables. Due to the high dimensionality, the solution of the arising algebraic systems do not become feasible without ...
Moataz Dawor, Nils Margenberg, Markus Bause   +2 more
wiley   +1 more source

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Median ensemble empirical mode decomposition

Signal Processing, 2020
Abstract Ensemble empirical mode decomposition (EEMD) belongs to a class of noise-assisted EMD methods that are aimed at alleviating mode mixing caused by noise and signal intermittency. In this work, we propose a median ensembled version of EEMD (MEEMD) to help reduce the additional mode splitting problem of the original EEMD algorithm.
Xun Lang, Naveed Ur Rehman, Hongye Su
exaly   +2 more sources

THE MULTI-DIMENSIONAL ENSEMBLE EMPIRICAL MODE DECOMPOSITION METHOD

Advances in Adaptive Data Analysis, 2009
A multi-dimensional ensemble empirical mode decomposition (MEEMD) for multi-dimensional data (such as images or solid with variable density) is proposed here. The decomposition is based on the applications of ensemble empirical mode decomposition (EEMD) to slices of data in each and every dimension involved.
Xianyao Chen, Zhaohua Wu, Norden E Huang   +2 more
exaly   +2 more sources

Performance enhancement of ensemble empirical mode decomposition

Mechanical Systems and Signal Processing, 2010
Ensemble empirical mode decomposition (EEMD) is a newly developed method aimed at eliminating mode mixing present in the original empirical mode decomposition (EMD). To evaluate the performance of this new method, this paper investigates the effect of two parameters pertinent to EEMD: the amplitude of added white noise and the number of ensemble trials.
Bin Ju, Ruqiang Yan, Robert Gao
exaly   +2 more sources

ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD

Advances in Adaptive Data Analysis, 2009
A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the ...
Zhaohua Wu, Norden E Huang
exaly   +2 more sources

Ensemble empirical mode decomposition for high frequency ECG noise reduction

Biomedizinische Technik, 2010
An electrocardiogram (ECG) is measured from the body surface and is often corrupted by various noises, such as high-frequency muscle contraction. Recently, empirical mode decomposition (EMD), a well-known analysis technique for nonlinear and non-stationary signals, has been employed for the purpose of ECG noise reduction. In this study, a modified EMD,
Kang-Ming Chang
exaly   +3 more sources

MODEL VALIDATION BASED ON ENSEMBLE EMPIRICAL MODE DECOMPOSITION

Advances in Adaptive Data Analysis, 2010
We proposed a new model validation method through ensemble empirical mode decomposition (EEMD) and scale separate correlation. EEMD is used to analyze the nonlinear and nonstationary ozone concentration data and the data simulated from the Taiwan Air Quality Model (TAQM).
Yu-Mei Chang, Zhaohua Wu, Julius Chang, Norden E. Huang   +3 more
openaire   +1 more source

ENSEMBLE EMPIRICAL MODE DECOMPOSITION FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Advances in Adaptive Data Analysis, 2012
Ensemble empirical mode decomposition (EEMD) is a novel adaptive time-frequency analysis method, which is particularly suitable for extracting useful information from noisy nonlinear or nonstationary data. This paper presents the utilization of EEMD for hyperspectral images to extract signals from them, generated in noisy nonlinear and nonstationary ...
Min Zhang, Yi Shen
openaire   +1 more source

A complete ensemble empirical mode decomposition with adaptive noise

2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2011
In this paper an algorithm based on the ensemble empirical mode decomposition (EEMD) is presented. The key idea on the EEMD relies on averaging the modes obtained by EMD applied to several realizations of Gaussian white noise added to the original signal. The resulting decomposition solves the EMD mode mixing problem, however it introduces new ones. In
María Eugenia Torres, Marcelo Alejandro Colominas, Gastón Schlotthauer, Patrick Flandrin   +3 more
openaire   +1 more source