SPAER: Sparse Deep Convolutional Autoencoder Model to Extract Low Dimensional Imaging Biomarkers for Early Detection of Breast Cancer Using Dynamic Thermography

Early diagnosis of breast cancer unequivocally improves the survival rate of patients and is crucial for disease treatment. With the current developments in infrared imaging, breast screening using dynamic thermography seems to be a great complementary method for clinical breast examination (CBE) prior to mammography. In this study, we propose a sparse deep convolutional autoencoder model named SPAER to extract low-dimensional deep thermomics to aid breast cancer diagnosis. The model receives multichannel, low-rank, approximated thermal bases as input images. SPAER provides a solution for high-dimensional deep learning features and selects the predominant basis matrix using matrix factorization techniques. The model has been evaluated using five state-of-the-art matrix factorization methods and 208 thermal breast cancer screening cases. The best accuracy was for non-negative matrix factorization (NMF)-SPAER + Clinical and NMF-SPAER for maintaining thermal heterogeneity, leading to finding symptomatic cases with accuracies of 78.2% (74.3–82.5%) and 77.7% (70.9–82.1%), respectively. SPAER showed significant robustness when tested for additive Gaussian noise cases (3–20% noise), evaluated by the signal-to-noise ratio (SNR). The results suggest high performance of SPAER for preserveing thermal heterogeneity, and it can be used as a noninvasive in vivo tool aiding CBE in the early detection of breast cancer.

Download Full-text

Adaptive-Weighted Multiview Deep Basis Matrix Factorization for Multimedia Data Analysis

Wireless Communications and Mobile Computing ◽

10.1155/2021/5526479 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Shicheng Li ◽

Qinghua Liu ◽

Jiangyan Dai ◽

Wenle Wang ◽

Xiaolin Gui ◽

...

Keyword(s):

Data Analysis ◽

Matrix Factorization ◽

Feature Learning ◽

Representation Learning ◽

Feature Representation ◽

Multimedia Data ◽

Basis Matrix ◽

Rich Information ◽

Low Dimensional ◽

Better Than

Feature representation learning is a key issue in artificial intelligence research. Multiview multimedia data can provide rich information, which makes feature representation become one of the current research hotspots in data analysis. Recently, a large number of multiview data feature representation methods have been proposed, among which matrix factorization shows the excellent performance. Therefore, we propose an adaptive-weighted multiview deep basis matrix factorization (AMDBMF) method that integrates matrix factorization, deep learning, and view fusion together. Specifically, we first perform deep basis matrix factorization on data of each view. Then, all views are integrated to complete the procedure of multiview feature learning. Finally, we propose an adaptive weighting strategy to fuse the low-dimensional features of each view so that a unified feature representation can be obtained for multiview multimedia data. We also design an iterative update algorithm to optimize the objective function and justify the convergence of the optimization algorithm through numerical experiments. We conducted clustering experiments on five multiview multimedia datasets and compare the proposed method with several excellent current methods. The experimental results demonstrate that the clustering performance of the proposed method is better than those of the other comparison methods.

Download Full-text

Instantaneous Versus Convolutive Non-Negative Matrix Factorization

Machine Audition ◽

10.4018/978-1-61520-919-4.ch015 ◽

2010 ◽

pp. 353-370 ◽

Cited By ~ 3

Author(s):

Wenwu Wang

Keyword(s):

Machine Learning ◽

Performance Evaluation ◽

Data Analysis ◽

Matrix Factorization ◽

Temporal Structure ◽

Low Rank ◽

Basis Matrix ◽

And Performance ◽

Separation Problems ◽

Non Negative Matrix Factorization

Non-negative matrix factorization (NMF) is an emerging technique for data analysis and machine learning, which aims to find low-rank representations for non-negative data. Early works in NMF are mainly based on the instantaneous model, i.e. using a single basis matrix to represent the data. Recent works have shown that the instantaneous model may not be satisfactory for many audio application tasks. The convolutive NMF model, which has an advantage of revealing the temporal structure possessed by many signals, has been proposed. This chapter intends to provide a brief overview of the models and algorithms for both the instantaneous and the convolutive NMF, with a focus on the theoretical analysis and performance evaluation of the convolutive NMF algorithms, and their applications to audio pattern separation problems.

Download Full-text

Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics

Biosensors ◽

10.3390/bios10110164 ◽

2020 ◽

Vol 10 (11) ◽

pp. 164

Author(s):

Bardia Yousefi ◽

Hamed Akbari ◽

Xavier P.V. Maldague

Keyword(s):

Breast Cancer ◽

Cancer Detection ◽

Diagnostic Technique ◽

Principal Component ◽

Low Rank ◽

Matrix Approximation ◽

Sparse Principal Component Analysis ◽

Latent Space ◽

Thermal Heterogeneity ◽

Concealed Information

Breast cancer is the most common cancer in women. Early diagnosis improves outcome and survival, which is the cornerstone of breast cancer treatment. Thermography has been utilized as a complementary diagnostic technique in breast cancer detection. Artificial intelligence (AI) has the capacity to capture and analyze the entire concealed information in thermography. In this study, we propose a method to potentially detect the immunohistochemical response to breast cancer by finding thermal heterogeneous patterns in the targeted area. In this study for breast cancer screening 208 subjects participated and normal and abnormal (diagnosed by mammography or clinical diagnosis) conditions were analyzed. High-dimensional deep thermomic features were extracted from the ResNet-50 pre-trained model from low-rank thermal matrix approximation using sparse principal component analysis. Then, a sparse deep autoencoder designed and trained for such data decreases the dimensionality to 16 latent space thermomic features. A random forest model was used to classify the participants. The proposed method preserves thermal heterogeneity, which leads to successful classification between normal and abnormal subjects with an accuracy of 78.16% (73.3–81.07%). By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy. These thermal signatures may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.

Download Full-text

General Collaborative Filtering for Web Service QoS Prediction

Mathematical Problems in Engineering ◽

10.1155/2018/5787406 ◽

2018 ◽

Vol 2018 ◽

pp. 1-18 ◽

Cited By ~ 2

Author(s):

Wenming Ma ◽

Rongjie Shan ◽

Mingming Qi

Keyword(s):

Collaborative Filtering ◽

Web Service ◽

Matrix Factorization ◽

Experimental Studies ◽

Low Rank ◽

Qos Prediction ◽

Latent Vector ◽

Wide Range ◽

Regularization Techniques ◽

Low Dimensional

To avoid the expensive and time-consuming evaluation, collaborative filtering (CF) methods have been widely studied for web service QoS prediction in recent years. Among the various CF techniques, matrix factorization is the most popular one. Much effort has been devoted to improving matrix factorization collaborative filtering. The key idea of matrix factorization is that it assumes the rating matrix is low rank and projects users and services into a shared low-dimensional latent space, making a prediction by using the dot product of a user latent vector and a service latent vector. Unfortunately, unlike the recommender systems, QoS usually takes continuous values with very wide range, and the low rank assumption might incur high bias. Furthermore, when the QoS matrix is extremely sparse, the low rank assumption also incurs high variance. To reduce the bias, we must use more complex assumptions. To reduce the variance, we can adopt complex regularization techniques. In this paper, we proposed a neural network based framework, named GCF (general collaborative filtering), with the dropout regularization, to model the user-service interactions. We conduct our experiments on a large real-world dataset, the QoS values of which are obtained from 339 users on 5825 web services. The comprehensive experimental studies show that our approach offers higher prediction accuracy than the traditional collaborative filtering approaches.

Download Full-text

Concentrated Thermomics for Early Diagnosis of Breast Cancer

Engineering Proceedings ◽

10.3390/engproc2021008030 ◽

2021 ◽

Vol 8 (1) ◽

pp. 30

Author(s):

Bardia Yousefi ◽

Michelle Hershman ◽

Henrique C. Fernandes ◽

Xavier P. V. Maldague

Keyword(s):

Breast Cancer ◽

Breast Cancer Diagnosis ◽

Clinical Breast ◽

Thermal Features ◽

Sparse Autoencoder ◽

Thermal Patterns ◽

Thermal Heterogeneity ◽

Demographic Covariates ◽

Low Dimensional ◽

Non Negative Matrix Factorization

Thermography has been employed broadly as a corresponding diagnostic instrument in breast cancer diagnosis. Among thermographic techniques, deep neural networks show an unequivocal potential to detect heterogeneous thermal patterns related to vasodilation in breast cancer cases. Such methods are used to extract high-dimensional thermal features, known as deep thermomics. In this study, we applied convex non-negative matrix factorization (convex NMF) to extract three predominant bases of thermal sequences. Then, the data were fed into a sparse autoencoder model, known as SPAER, to extract low-dimensional deep thermomics, which were then used to assist the clinical breast exam (CBE) in breast cancer screening. The application of convex NMF-SPAER, combining clinical and demographic covariates, yielded a result of 79.3% (73.5%, 86.9%); the highest result belonged to NMF-SPAER at 84.9% (79.3%, 88.7%). The proposed approach preserved thermal heterogeneity and led to early detection of breast cancer. It can be used as a noninvasive tool aiding CBE.

Download Full-text