2021-HBA-U-Net

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S Tang, Z Qi, J Granley, M Beyeler (2021). U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina. MICCAI OMIA8 Workshop 2021, online.

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# U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina

Please cite as:

> S Tang, Z Qi, J Granley, M Beyeler (2021). U-Net with Hierarchical Bottleneck Attention for Landmark Detection in Fundus Images of the Degenerated Retina. *MICCAI OMIA8 Workshop, online*.

The preprint can be found on [arXiv](https://arxiv.org/abs/2107.04721) and the published paper [here](https://doi.org/10.1007/978-3-030-87000-3_7).

Before enshittification took over open-source AI, HBA-U-Net was listed as the state of the art (SOTA) for several popular datasets of retinal degeneration ([Papers with Code](https://paperswithcode.com/paper/u-net-with-hierarchical-bottleneck-attention), retrieved on May 12, 2025):

- #1 ADAM: Fovea detection
- #1 ADAM: Optic disc segmentation
- #1 IDRiD: Fovea detection
- #1 IDRiD: Optic disc detection
- #1 REFUGE: Fovea detection
- #2 REFUGE: Optic disc segmentation

### Requirements

- Python 3
- Keras 2.4.3
- TensorFlow 2.5.0
- Scikit-Learn 0.22
- Skimage 0.16.2
- cv2 4.1.2
- PIL 7.1.2
- Pandas 1.1.5

### How to acquire the private dataset listed in the paper

- [ADAM](https://amd.grand-challenge.org/)
- [REFUGE](https://refuge.grand-challenge.org/details/)
- [IDRiD](https://idrid.grand-challenge.org/)

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