A Fast Stain Normalization Network for Cervical Papanicolaou Images


International Conference on Neural Information Processing (ICONIP 2022)


Jiawei Cao, Changsheng Lu, Kaijie Wu, Chaochen Gu


The domain shift between different styles of stain images greatly challenges the generalization of computer-aided diagnosis (CAD) algorithms. To bridge the gap, color normalization is a prerequisite for most CAD algorithms. The existing algorithms with better normalization effect often require more computational consumption, resisting the fast application in large-size medical stain slide images. This paper designs a fast normalization network (FTNC-Net) for cervical Papanicolaou stain images based on learnable bilateral filtering. In our FTNC-Net, explicit three-attribute estimation and spatially adaptive instance normalization are introduced to guide the model to transfer stain color styles in space accurately, and dynamic blocks are adopted to adapt multiple stain color styles. Our method achieves at least 80 fps over 1024×1024 images on our experimental platform, thus it can synchronize with the scanner for image acquisition and processing, and has advantages in visual and quantitative evaluation compared with other methods. Moreover, experiments on our cervical staining image dataset demonstrate that the FTNC-Net improves the precision of abnormal cell detection.

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