Deformable Gabor Feature Networks for Biomedical Image Classification
- URL: http://arxiv.org/abs/2012.04109v1
- Date: Mon, 7 Dec 2020 23:25:32 GMT
- Title: Deformable Gabor Feature Networks for Biomedical Image Classification
- Authors: Xuan Gong, Xin Xia, Wentao Zhu, Baochang Zhang, David Doermann, Lian
Zhuo
- Abstract summary: We introduce a deformable Gabor convolution (DGConv) to expand deep networks interpretability and enable complex spatial variations.
The DGConv replaces standard convolutional layers and is easily trained end-to-end.
We introduce DGFN for addressing deep multi-instance multi-label classification on the INbreast dataset for mammograms and on the ChestX-ray14 dataset for pulmonary x-ray images.
- Score: 40.13594024532627
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In recent years, deep learning has dominated progress in the field of medical
image analysis. We find however, that the ability of current deep learning
approaches to represent the complex geometric structures of many medical images
is insufficient. One limitation is that deep learning models require a
tremendous amount of data, and it is very difficult to obtain a sufficient
amount with the necessary detail. A second limitation is that there are
underlying features of these medical images that are well established, but the
black-box nature of existing convolutional neural networks (CNNs) do not allow
us to exploit them. In this paper, we revisit Gabor filters and introduce a
deformable Gabor convolution (DGConv) to expand deep networks interpretability
and enable complex spatial variations. The features are learned at deformable
sampling locations with adaptive Gabor convolutions to improve
representativeness and robustness to complex objects. The DGConv replaces
standard convolutional layers and is easily trained end-to-end, resulting in
deformable Gabor feature network (DGFN) with few additional parameters and
minimal additional training cost. We introduce DGFN for addressing deep
multi-instance multi-label classification on the INbreast dataset for
mammograms and on the ChestX-ray14 dataset for pulmonary x-ray images.
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