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Proceedings Paper

Micronucleus image recognition based on feature-map spatial transformation
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Paper Abstract

Convolutional neural networks in deep learning models have dominated the recent image recognition works. But the lack of capacity to maintain spatial invariance makes identification of micronucleus cells as a classic task in digital pathology still a challenge task. In this paper, a novel convolutional neural network for feature maps spatial transformation (FSTCNN) is proposed, which incorporates a Spatial Transformer Network. Our model allows the spatial manipulation of data within the network, provides the ability of active spatial transformation for neural network without any extra supervision. We compared the results of inserting STN into different convolutional layers and found that such a network can transform the input image more steadily, correct the image to one certain position, make it fill the whole screen to create a better environment for image recognition. The results show a distinct advantage over other convolutional neural networks for medical image recognition.

Paper Details

Date Published: 14 August 2019
PDF: 8 pages
Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791X (14 August 2019); doi: 10.1117/12.2540468
Show Author Affiliations
Yujie Xu, Wuhan Univ. of Technology (China)
Jiwei Hu, Wuhan Univ. of Technology (China)
Quan Liu, Wuhan Univ. of Technology (China)
Jiamei Deng, Wuhan Univ. of Technology (China)


Published in SPIE Proceedings Vol. 11179:
Eleventh International Conference on Digital Image Processing (ICDIP 2019)
Jenq-Neng Hwang; Xudong Jiang, Editor(s)

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