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- W4288333138 abstract "Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and variations thereof have been introduced to recalibrate feature maps channel- and spatial-wise, which can boost performance while only minimally increasing model complexity. So far, the development of SE has focused on 2D images. In this paper, we propose `Project & Excite' (PE) modules that base upon the ideas of SE and extend them to operating on 3D volumetric images. `Project & Excite' does not perform global average pooling, but squeezes feature maps along different slices of a tensor separately to retain more spatial information that is subsequently used in the excitation step. We demonstrate that PE modules can be easily integrated in 3D U-Net, boosting performance by 5% Dice points, while only increasing the model complexity by 2%. We evaluate the PE module on two challenging tasks, whole-brain segmentation of MRI scans and whole-body segmentation of CT scans. Code: https://github.com/ai-med/squeeze_and_excitation" @default.
- W4288333138 created "2022-07-28" @default.
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- W4288333138 date "2019-06-11" @default.
- W4288333138 modified "2023-10-03" @default.
- W4288333138 title "`Project & Excite' Modules for Segmentation of Volumetric Medical Scans" @default.
- W4288333138 doi "https://doi.org/10.48550/arxiv.1906.04649" @default.
- W4288333138 hasPublicationYear "2019" @default.
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