The Experts below are selected from a list of 153150 Experts worldwide ranked by ideXlab platform

Zheng Zhiyin - One of the best experts on this subject based on the ideXlab platform.

  • deep convolutional neural network based lung cancer preventing self service health cloud service system
    2017
    Co-Authors: Tang Yiping, Zheng Zhiyin
    Abstract:

    The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system. The system comprises a convolutional neural network used for deep learning and training identification, a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network, a deep convolutional neural network used for lung cancer diagnosis classification, and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type. According to the system, the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved, more citizens can know and participate in self-service health detection, assessment and guidance, the sensitivity, specificity and accuracy of early lung cancer screening and clinical diagnosis are improved, the lung cancer can be early discovered, early diagnosed and early treated, and the self-health management capability is enhanced.

  • breast cancer prevention self service health cloud service system based on deep convolutional neural network
    2017
    Co-Authors: Tang Yiping, Zheng Zhiyin
    Abstract:

    The invention discloses a breast cancer prevention self-service health cloud service system based on a deep convolutional neural network. The self-service health cloud service system mainly comprises a convolutional neural network applied to deep learning and training recognition, a partitioning module which is used for partitioning a breast area from a mammary gland molybdenum target radiography image based on a full-convolutional neural network, the deep convolutional neural network which is applied to BI-RADS classification and evaluation, and a self-service health cloud service platform which is applied to early preventing and treating breast cancer according to a recognized mammary gland inner structure, tumor and calcification types. The self-service health cloud service system can effectively improve the automatic and intelligent level of breast cancer screening based on a mobile internet, enable more women to know and participate in self-service health detection, evaluation and guidance, further improve the health consciousness of the public, and improve self health management capacity.

Jure žbontar - One of the best experts on this subject based on the ideXlab platform.

  • stereo matching by training a convolutional neural network to compare image patches
    Journal of Machine Learning Research, 2016
    Co-Authors: Jure žbontar, Yann Lecun
    Abstract:

    We present a method for extracting depth information from a rectified image pair. Our approach focuses on the first stage of many stereo algorithms: the matching cost computation. We approach the problem by learning a similarity measure on small image patches using a convolutional neural network. Training is carried out in a supervised manner by constructing a binary classification data set with examples of similar and dissimilar pairs of patches. We examine two network architectures for this task: one tuned for speed, the other for accuracy. The output of the convolutional neural network is used to initialize the stereo matching cost. A series of post-processing steps follow: cross-based cost aggregation, semiglobal matching, a left-right consistency check, subpixel enhancement, a median filter, and a bilateral filter. We evaluate our method on the KITTI 2012, KITTI 2015, and Middlebury stereo data sets and show that it outperforms other approaches on all three data sets.

  • Computing the stereo matching cost with a convolutional neural network
    Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2015
    Co-Authors: Jure žbontar, Yue Cun
    Abstract:

    We present a method for extracting depth information from a rectified image pair. We train a convolutional neural network to predict how well two image patches match and use it to compute the stereo matching cost. The cost is refined by cross-based cost aggregation and semiglobal matching, followed by a left-right consistency check to eliminate errors in the occluded regions. Our stereo method achieves an error rate of 2.61 % on the KITTI stereo dataset and is currently (August 2014) the top performing method on this dataset.

Tang Yiping - One of the best experts on this subject based on the ideXlab platform.

  • deep convolutional neural network based lung cancer preventing self service health cloud service system
    2017
    Co-Authors: Tang Yiping, Zheng Zhiyin
    Abstract:

    The invention discloses a deep convolutional neural network-based lung cancer preventing self-service health cloud service system. The system comprises a convolutional neural network used for deep learning and training identification, a segmentation module which segments out a lung region from a CT image based on a full convolutional neural network, a deep convolutional neural network used for lung cancer diagnosis classification, and a self-service health cloud service platform used for performing early prevention and treatment according to an identified suspected lung cancer type. According to the system, the automation and intelligentization level of mobile internet-based lung cancer screening can be effectively improved, more citizens can know and participate in self-service health detection, assessment and guidance, the sensitivity, specificity and accuracy of early lung cancer screening and clinical diagnosis are improved, the lung cancer can be early discovered, early diagnosed and early treated, and the self-health management capability is enhanced.

  • breast cancer prevention self service health cloud service system based on deep convolutional neural network
    2017
    Co-Authors: Tang Yiping, Zheng Zhiyin
    Abstract:

    The invention discloses a breast cancer prevention self-service health cloud service system based on a deep convolutional neural network. The self-service health cloud service system mainly comprises a convolutional neural network applied to deep learning and training recognition, a partitioning module which is used for partitioning a breast area from a mammary gland molybdenum target radiography image based on a full-convolutional neural network, the deep convolutional neural network which is applied to BI-RADS classification and evaluation, and a self-service health cloud service platform which is applied to early preventing and treating breast cancer according to a recognized mammary gland inner structure, tumor and calcification types. The self-service health cloud service system can effectively improve the automatic and intelligent level of breast cancer screening based on a mobile internet, enable more women to know and participate in self-service health detection, evaluation and guidance, further improve the health consciousness of the public, and improve self health management capacity.

Lin Yang - One of the best experts on this subject based on the ideXlab platform.

  • region segmentation in histopathological breast cancer images using deep convolutional neural network
    International Symposium on Biomedical Imaging, 2015
    Co-Authors: Hai Su, Fujun Liu, Yuanpu Xie, Fuyong Xing, Sreenivasan Meyyappan, Lin Yang
    Abstract:

    Computer aided diagnosis of breast cancers often relies on automatic image analysis of histopathology images. The automatic region segmentation in breast cancer is challenging due to: i) large regional variations, and ii) high computational costs of pixel-wise segmentation. Deep convolutional neural network (CNN) is proven to be an effective method for image recognition and classification. However, it is often computationally expensive. In this paper, we propose to apply a fast scanning deep convolutional neural network (fCNN) to pixel-wise region segmentation. The fCNN removes the redundant computations in the original CNN without sacrificing its performance. In our experiment it takes only 2.3 seconds to segment an image with size 1000 × 1000. The comparison experiments show that the proposed system outperforms both the LBP feature-based and texton-based pixel-wise methods.

Andrew D. Back - One of the best experts on this subject based on the ideXlab platform.

  • Face recognition: A convolutional neural-network approach
    IEEE Transactions on Neural Networks, 1997
    Co-Authors: Steve Lawrence, C. Lee Giles, Ah Chung Tsoi, Andrew D. Back
    Abstract:

    We present a hybrid neural-network for human face recognition which compares favourably with other methods. The system combines local image sampling, a self-organizing map (SOM) neural network, and a convolutional neural network. The SOM provides a quantization of the image samples into a topological space where inputs that are nearby in the original space are also nearby in the output space, thereby providing dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides partial invariance to translation, rotation, scale, and deformation. The convolutional network extracts successively larger features in a hierarchical set of layers. We present results using the Karhunen-Loeve transform in place of the SOM, and a multilayer perceptron (MLP) in place of the convolutional network for comparison. We use a database of 400 images of 40 individuals which contains quite a high degree of variability in expression, pose, and facial details. We analyze the computational complexity and discuss how new classes could be added to the trained recognizer.