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Neurocomputing v.229, 2017년, pp.34 - 44   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features

Wan, Tao (School of Biomedical Science and Medical Engineering, Beihang University, Beijing 100191, China ); Cao, Jiajia (Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China ); Chen, Jianhui (No. 91 Central Hospital of PLA, Henan 454000, China ); Qin, Zengchang (Intelligent Computing and Machine Learning Lab, School of Automation Science and Electrical Engineering, Beihang University, Beijing 100191, China );
  • 초록  

    Abstract We present a novel image-analysis based method for automatically distinguishing low, intermediate, and high grades of breast cancer in digitized histopathology. A multiple level feature set, including pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN), is extracted from 106 hematoxylin and eosin stained breast biopsy tissue studies from 106 women patients. These multi-level features allow not only characterization of cancer morphology, but also extraction of structural and interpretable information within the histopathological images. In this study, an improved hybrid active contour model based segmentation method was used to segment nuclei from the images. The semantic-level features were extracted by a CNN approach, which described the proportions of nuclei belonging to the different grades, in conjunction with pixel-level (texture) and object-level (architecture) features, to create an integrated set of image attributes that can potentially outperform either subtype of features individually. We utilized a cascaded approach to train multiple support vector machine (SVM) classifiers using combinations of feature subtypes to enable the possibility of maximizing the performance by leveraging different feature sets extracted from multiple levels. Final class (cancer grade) was determined by combining the scores produced by the individual SVM classifiers. By employing a light (three-layer) CNN model and parallel computing, the presented approach is computationally efficient and applicable to large-scale datasets. The method achieved an accuracy of 0.92 for low versus high, 0.77 for low versus intermediate, and 0.76 for intermediate versus high, and an overall accuracy of 0.69 when discriminating low, intermediate, and high grades of histopathological breast cancer images. This suggested that our grading method could be useful in developing a computational diagnostic tool for differentiating breast cancer grades, which might enable objective and reproducible alternative for diagnosis. Highlights An automated breast cancer grading method in histopathology is presented. Multi-level features are extracted to capture histomorphometric attributes in histology. The grading method greatly improves the breast cancer grading performance.


  • 주제어

    Breast cancer grading .   Histopathology .   Multi-level features .   Convolutional neural networks .   Cascaded ensemble.  

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