# Boundary and Entropy-driven Adversarial Learning for Fundus Image Segmentation

arXiv: https://arxiv.org/abs/1906.11143 · HTML: https://mlsanity.com/paper/1906.11143/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

## Built on by (most cited first, 30 of 46)

- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model](https://mlsanity.com/paper/2211.00611/) (2022)
- [Medical SAM 2: Segment medical images as video via Segment Anything Model 2](https://mlsanity.com/paper/2408.00874/) (2024)
- [MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer](https://mlsanity.com/paper/2301.11798/) (2023)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling](https://mlsanity.com/paper/2109.09735/) (2021)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [REFUGE2 Challenge: A Treasure Trove for Multi-Dimension Analysis and Evaluation in Glaucoma Screening](https://mlsanity.com/paper/2202.08994/) (2022)
- [Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation](https://mlsanity.com/paper/2109.05676/) (2021)
- [Each Test Image Deserves A Specific Prompt: Continual Test-Time Adaptation for 2D Medical Image Segmentation](https://mlsanity.com/paper/2311.18363/) (2023)
- [Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention](https://mlsanity.com/paper/2405.06284/) (2024)
- [Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives](https://mlsanity.com/paper/2308.01265/) (2023)
- [Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher](https://mlsanity.com/paper/2307.09973/) (2023)
- [ProSFDA: Prompt Learning based Source-free Domain Adaptation for Medical Image Segmentation](https://mlsanity.com/paper/2211.11514/) (2022)
- [Target and Task specific Source-Free Domain Adaptive Image Segmentation](https://mlsanity.com/paper/2203.15792/) (2022)
- [Adapt Everywhere: Unsupervised Adaptation of Point-Clouds and Entropy Minimisation for Multi-modal Cardiac Image Segmentation](https://mlsanity.com/paper/2103.08219/) (2021)
- [Learning self-calibrated optic disc and cup segmentation from multi-rater annotations](https://mlsanity.com/paper/2206.05092/) (2022)
- [Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation](https://mlsanity.com/paper/2308.07731/) (2023)
- [Unsupervised Domain Adaptive Fundus Image Segmentation with Category-level Regularization](https://mlsanity.com/paper/2207.03684/) (2022)
- [Reconstruction-driven Dynamic Refinement based Unsupervised Domain Adaptation for Joint Optic Disc and Cup Segmentation](https://mlsanity.com/paper/2304.04581/) (2023)
- [Timeline and Boundary Guided Diffusion Network for Video Shadow Detection](https://mlsanity.com/paper/2408.11785/) (2024)
- [A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation](https://mlsanity.com/paper/2310.18087/) (2023)
- [Rapid Image Labeling via Neuro-Symbolic Learning](https://mlsanity.com/paper/2306.10490/) (2023)
- [Towards Clinician-Preferred Segmentation: Leveraging Human-in-the-Loop for Test Time Adaptation in Medical Image Segmentation](https://mlsanity.com/paper/2405.08270/) (2024)
- [Diffusion Model as a Generalist Segmentation Learner](https://mlsanity.com/paper/2604.24575/) (2026)
- [Opinions Vary? Diagnosis First!](https://mlsanity.com/paper/2202.06505/) (2022)
- [Calibrate the inter-observer segmentation uncertainty via diagnosis-first principle](https://mlsanity.com/paper/2208.03016/) (2022)
- [Unsupervised Domain Adaptive Fundus Image Segmentation with Few Labeled Source Data](https://mlsanity.com/paper/2210.04379/) (2022)
- [Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters](https://mlsanity.com/paper/2212.00601/) (2022)
- [Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation](https://mlsanity.com/paper/2305.07881/) (2023)
- [HCDG: A Hierarchical Consistency Framework for Domain Generalization on Medical Image Segmentation](https://mlsanity.com/paper/2109.05742/) (2021)

## Abstract (arXiv)

Accurate segmentation of the optic disc (OD) and cup (OC)in fundus images from different datasets is critical for glaucoma disease screening. The cross-domain discrepancy (domain shift) hinders the generalization of deep neural networks to work on different domain datasets.In this work, we present an unsupervised domain adaptation framework,called Boundary and Entropy-driven Adversarial Learning (BEAL), to improve the OD and OC segmentation performance, especially on the ambiguous boundary regions. In particular, our proposed BEAL frame-work utilizes the adversarial learning to encourage the boundary prediction and mask probability entropy map (uncertainty map) of the target domain to be similar to the source ones, generating more accurate boundaries and suppressing the high uncertainty predictions of OD and OC segmentation. We evaluate the proposed BEAL framework on two public retinal fundus image datasets (Drishti-GS and RIM-ONE-r3), and the experiment results demonstrate that our method outperforms the state-of-the-art unsupervised domain adaptation methods. Codes will be available at https://github.com/EmmaW8/BEAL.