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Locally Rotation Invariant (LRI) operators have shown great potential to analyze biomedical textures where discriminative patterns appear at random positions and orientations. We build LRI operators through the local projection of the image on circular harmonics followed by the computation of the bispectrum, which is LRI by design. This formulation allows to avoid the discretization of the orientations and does not require any criterion to locally align the descriptors. This operator is used in a convolutional layer resulting in LRI Convolutional Neural Networks (LRI CNN). To evaluate the relevance of this approach, we use it to segment cellular nuclei in histopathological images. We compare the proposed bispectral LRI layer against a standard convolutional layer in a U-Net architecture. While they perform equally in terms of F-score, the LRI CNN provides more robust segmentation with respect to orientation, even when rotational data augmentation is used. This robustness is essential when the relevant pattern may vary in orientation, which is often the case in medical images. Keywords: Locally Rotation Invariant, Convolutional Neural Network, Deep Learning, Segmentation, Bispectrum.
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Thursday 7th July
Poster Session 2.2 - onsite 11:00 - 12:00, virtual 15:20 - 16:20 (UTC+2)
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