Composition statistics of gynecological microscopic images based on texture analysis and medical intelligence system
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials
Link
https://link.springer.com/content/pdf/10.1007/s11082-023-05766-w.pdf
Reference17 articles.
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3. Gu, Z., Cheng, J., Fu, H., et al.: CE-net: context encoder network for 2D medical image segmentation. IEEE Trans. Med. Imaging 38(10), 2281–2292 (2019)
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5. Isensee, F., Jaeger, P.F., Kohl, S.A.A., Petersen, J., Maier-Hein, K.H.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203–211 (2021)
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