Hybrid Subspace Fusion for Microcalcification Clusters Detection

  title={Hybrid Subspace Fusion for Microcalcification Clusters Detection},
  author={Xinsheng Zhang and Hongyan He and Naining Cao and Zhengshan Luo},
  journal={Journal of Fiber Bioengineering and Informatics},
Early detection of breast cancer, a significant public health problem in the world, is the key for improving breast cancer early prognosis. Mammography is considered the most reliable and widely used diagnostic technique for early detection of breast cancer. However, it is difficult for radiologists to perform both accurate and uniform evaluation for the enormous mammograms with widespread screening. Microcalcification clusters is one of the most important clue of the breast cancer, and their… 
Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.
  • Huiya Wang, Jun Feng, Hongyu Wang
  • Computer Science, Medicine
    Technology and health care : official journal of the European Society for Engineering and Medicine
  • 2017
Experimental results demonstrate that the integrated classification framework incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification.


A Novel Approach to Detect Microcalcification in Mammogram Image using Evolutionary Algorithm
The proposed GA, ABC and Bilateral algorithms are quite competitive with the other algorithms and the basic idea of the asymmetry approach is corresponding left and right images are subtracted to extract the suspicious region.
Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection
A CAD model based on computer vision procedures for locating suspicious regions that are later analyzed by artificial neural networks, support vector machines and linear discriminant analysis, to classify them into benign or malignant, based on a set of features that are extracted from lesions to characterize their visual content is presented.
Automatic classification of clustered microcalcifications by a multiple expert system
This paper proposes a novel approach for classifying clusters of microcalcifications, based on a Multiple Expert System; such system aggregates several experts, some of which are devoted to classify the single microCalcifications while others are aimed to classified the cluster considered as a whole.
A New Approach for Clustered MCs Classification with Sparse Features Learning and TWSVM
  • Xinsheng Zhang
  • Computer Science, Medicine
  • 2014
A new approach is proposed to classify and detect microcalcification clusters in mammograms as sparse feature learning based classification on behalf of the test samples with a set of training samples, which are also known as a “vocabulary” of visual parts.
Twin support vector machines and subspace learning methods for microcalcification clusters detection
Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis.
Decision fusion directly optimized clinically significant performance measures, such as AUC and pAUC, and sometimes outperformed two well-known machine-learning techniques when applied to two different breast cancer data sets.
Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples
  • Ming Li, Zhi-Hua Zhou
  • Computer Science
    IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans
  • 2007
Case studies on three medical data sets and a successful application to microcalcification detection for breast cancer diagnosis show that undiagnosed samples are helpful in building CAD systems, and Co-Forest is able to enhance the performance of the hypothesis that is learned on only a small amount of diagnosed samples by utilizing the available undiognosed samples.
Learning nonlinear image manifolds by global alignment of local linear models
  • J. Verbeek
  • Mathematics, Computer Science
    IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2006
A parameter estimation scheme that improves upon an existing scheme and experimentally compare the presented approach to self-organizing maps, generative topographic mapping, and mixtures of factor analyzers is compared.
Visualizing Data using t-SNE
We present a new technique called “t-SNE” that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map. The technique is a variation of Stochastic