# Deformable Gabor Feature Networks for Biomedical Image Classification

arXiv: https://arxiv.org/abs/2012.04109 · HTML: https://mlsanity.com/paper/2012.04109/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2020.

## Built on by (most cited first, 10 of 10)

- [MH-pFLID: Model Heterogeneous personalized Federated Learning via Injection and Distillation for Medical Data Analysis](https://mlsanity.com/paper/2405.06822/) (2024)
- [Chest X-ray Image Classification: A Causal Perspective](https://mlsanity.com/paper/2305.12072/) (2023)
- [Deep Reinforcement Learning Framework for Thoracic Diseases Classification via Prior Knowledge Guidance](https://mlsanity.com/paper/2306.01232/) (2023)
- [Medical supervised masked autoencoders: Crafting a better masking strategy and efficient fine-tuning schedule for medical image classification](https://mlsanity.com/paper/2305.05871/) (2023)
- [Instrumental Variable Learning for Chest X-ray Classification](https://mlsanity.com/paper/2305.12070/) (2023)
- [KFWC: A Knowledge-Driven Deep Learning Model for Fine-grained Classification of Wet-AMD](https://mlsanity.com/paper/2112.12386/) (2021)
- [Efficient Masked Attention Transformer for Few-Shot Classification and Segmentation](https://mlsanity.com/paper/2507.23642/) (2025)
- [Evaluating Federated Learning approaches for mammography under breast density heterogeneity](https://mlsanity.com/paper/2605.09137/) (2026)
- [MSA-DCNN: A Data-Efficient Multi-Scale Attention Deformable CNN for Medical Image Classification](https://mlsanity.com/paper/2607.06083/) (2026)
- [Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection](https://mlsanity.com/paper/2609.10988/) (2026)

## Abstract (arXiv)

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.