title: Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.
abstract: Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture.
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China. Electronic address: lisiqi881020@163.com.
authors.forename: Siqi
authors.initials: S
authors.lastname: Li
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China. Electronic address: hyjiang@mail.neu.edu.cn.
authors.forename: Huiyan
authors.initials: H
authors.lastname: Jiang
authors.affiliation: Software College, Northeastern University, Shenyang 110819, China. Electronic address: 358387616@qq.com.
authors.forename: Wenbo
authors.initials: W
authors.lastname: Pang
mesh_terms: D000465:Algorithms; D001707:Biopsy, Needle; D006528:Carcinoma, Hepatocellular / Q000175:diagnosis* / Q000473:pathology; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006651:Histocytochemistry; D006801:Humans; D008099:Liver / Q000473:pathology*; D008113:Liver Neoplasms / Q000175:diagnosis* / Q000473:pathology; D000069550:Machine Learning; D060787:Neoplasm Grading; D016571:Neural Networks, Computer
keywords: Back propagation; Convolutional neural network; Extreme learning machine; Hepatocellular carcinoma nuclei grading; Multiple fully connected layers