A Prototype Learning Based Multi-Instance Convolutional Neural Network

被引：0
|
作者：
He K.-L. [1 ]
Shi Y.-H. [1 ]
Gao Y. [1 ]
Huo J. [1 ]
Wang D. [2 ]
Zhang Y. [2 ]
机构：
[1] State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing
[2] Bayi Hospital, Nanjing
来源：
Gao, Yang (gaoy@nju.edu.cn) | 2017年 / Science Press卷 / 40期
基金：
中国国家自然科学基金;
关键词：
Artificial intelligence; Convolutional neural network; Deep learning; Image classification; Multi-instance learning; Prototype learning;
D O I：
10.11897/SP.J.1016.2017.01265
中图分类号：
学科分类号：
摘要：
Convolutional neural network is a fully supervised deep learning model. It requires that the labels of samples are fully provided. In weakly supervised applications where labels of samples are partly provided, the usage of convolutional neural networks is greatly limited. To solve the weakly supervised multi-instance learning problem, a new multiple instance convolutional neural network is proposed. The proposed model introduces a new prototype learning layer into the network. The prototype learning layer uses a prototype based metric method to transform instance features into bag features. The network therefore can use label information of bag and learning the whole model in a compact process. The network is firstly tested on a lung cancer cell pathology image classification dataset. Results show, compared with hand designed image feature based methods, the proposed method achieved an improvement of about 12% in accuracy. Compared with convolutional neural network and multi-instance learning combined methods, the proposed method also achieved better results on all the evaluation criterion. Besides, the method is also tested on a natural image classification dataset (GRAZ-02). Comparable result is achieved by the proposed method compared with the state-of-the-art method. © 2017, Science Press. All right reserved.
引用
收藏
页码：1265 / 1274
页数：9
相关论文
共 29 条
  • [1] Hinton G.E., Deng L., Yu D., Et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, IEEE Transactions on Signal Processing Magazine, 29, 6, pp. 82-97, (2012)
  • [2] Sun Y., Chen Y., Wang X., Et al., Deep learning face representation by joint identification-verification, Proceedings of the Advances in Neural Information Processing Systems, pp. 1988-1996, (2014)
  • [3] Taigman Y., Yang M., Ranzato M.A., Et al., Deepface: Closing the gap to human-level performance in face verification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1701-1708, (2014)
  • [4] Li W., Duan L., Xu D., Et al., Text-based image retrieval using progressive multi-instance learning, Proceedings of the IEEE International Conference on Computer Vision, pp. 2049-2055, (2011)
  • [5] Andrews S., Tsochantaridis I., Hofmann T., Support vector machines for multiple-instance learning, Proceedings of the Advances in Neural Information Processing Systems, pp. 561-568, (2002)
  • [6] Han Y., Tao Q., Wang J., Avoiding false positive in multi-instance learning, Proceedings of the Advances in Neural Information Processing Systems, pp. 811-819, (2010)
  • [7] Dietterich T.G., Lathrop R.H., Lozano-Perez T., Solving the multiple instance problem with axis-parallel rectangles, Artificial Intelligence, 89, 1, pp. 31-71, (1997)
  • [8] Zha Z.J., Hua X.S., Mei T., Et al., Joint multi-label multi-instance learning for image classification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-8, (2008)
  • [9] Zhou Z.H., Zhang M.L., Chen K.J., A novel bag generator for image database retrieval with multi-instance learning techniques, Proceedings of the IEEE International Conference on Tools with Artificial Intelligence, pp. 565-569, (2003)
  • [10] Krizhevsky A., Sutskever I., Hinton G.E., Imagenet classification with deep convolutional neural networks, Proceedings of the Advances in Neural Information Processing Systems, pp. 1097-1105, (2012)
123