Micronucleus image recognition based on feature-map spatial transformation

Yujie Xu; Jiwei Hu; Quan Liu; Jiamei Deng

doi:10.1117/12.2540468

14 August 2019 Micronucleus image recognition based on feature-map spatial transformation

Yujie Xu, Jiwei Hu, Quan Liu, Jiamei Deng

Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 111791X (2019) https://doi.org/10.1117/12.2540468
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China

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.

Citation Download Citation

Yujie Xu, Jiwei Hu, Quan Liu, and Jiamei Deng "Micronucleus image recognition based on feature-map spatial transformation", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 111791X (14 August 2019); https://doi.org/10.1117/12.2540468

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KEYWORDS

Transformers

Neural networks

Convolution

Visual process modeling

Convolutional neural networks

Data modeling

Feature extraction

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