# Deformable Gabor Feature Networks for Biomedical Image Classification Source: [Deformable Gabor Feature Networks for Biomedical Image Classification]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:48:52.480Z Authors: Xuan Gong, Xin Xia, Wentao Zhu, Baochang Zhang, David Doermann, Lian Zhuo Published: 2020-12-07T23:25:32.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on ISIC 2020](): Image Classification; ISIC 2020; Accuracy: 94. - [Image Classification on C-NMC](): Image Classification; C-NMC; AUC: 94. - [Image Classification on ALL (Hold-Out)](): Image Classification; ALL (Hold-Out); AUC: 91. - [Image Classification on PBC](): Image Classification; PBC; Accuracy: 92. ## Original sources and implementation [Original paper]() [PDF]()