# ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation

arXiv: https://arxiv.org/abs/2312.05407 · HTML: https://mlsanity.com/paper/2312.05407/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 42 references that are in the map.
- First posted on arXiv 8 Dec 2023.

## Builds on (most cited first, 26 of 26)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Deep Bayesian Active Learning with Image Data](https://mlsanity.com/paper/1703.02910/) (2017)
- [Continual Test-Time Domain Adaptation](https://mlsanity.com/paper/2203.13591/) (2022)
- [Efficient Test-Time Model Adaptation without Forgetting](https://mlsanity.com/paper/2204.02610/) (2022)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentation](https://mlsanity.com/paper/1902.04049/) (2019)
- [CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2102.09559/) (2021)
- [Cost-Effective Active Learning for Deep Image Classification](https://mlsanity.com/paper/1701.03551/) (2017)
- [Deep Semantic Segmentation of Natural and Medical Images: A Review](https://mlsanity.com/paper/1910.07655/) (2019)
- [MS-Net: Multi-Site Network for Improving Prostate Segmentation with Heterogeneous MRI Data](https://mlsanity.com/paper/2002.03366/) (2020)
- [Deep Active Learning for Named Entity Recognition](https://mlsanity.com/paper/1707.05928/) (2017)
- [Test-Time Adaptable Neural Networks for Robust Medical Image Segmentation](https://mlsanity.com/paper/2004.04668/) (2020)
- [Towards Fewer Annotations: Active Learning via Region Impurity and Prediction Uncertainty for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2111.12940/) (2021)
- [Active Learning for Domain Adaptation: An Energy-Based Approach](https://mlsanity.com/paper/2112.01406/) (2021)
- [Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation](https://mlsanity.com/paper/2101.02323/) (2021)
- [LabOR: Labeling Only if Required for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2108.05570/) (2021)
- [Studying Robustness of Semantic Segmentation under Domain Shift in cardiac MRI](https://mlsanity.com/paper/2011.07592/) (2020)
- [DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images](https://mlsanity.com/paper/2205.13723/) (2022)
- [LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation](https://mlsanity.com/paper/2212.02078/) (2022)
- [Generalized Domain Conditioned Adaptation Network](https://mlsanity.com/paper/2103.12339/) (2021)
- [Active learning for medical image segmentation with stochastic batches](https://mlsanity.com/paper/2301.07670/) (2023)
- [Test-Time Adaptation with Shape Moments for Image Segmentation](https://mlsanity.com/paper/2205.07983/) (2022)
- [Scale-aware Test-time Click Adaptation for Pulmonary Nodule and Mass Segmentation](https://mlsanity.com/paper/2307.15645/) (2023)

## Built on by (most cited first, 1 of 1)

- [EviATTA: Evidential Active Test-Time Adaptation for Medical Segment Anything Models](https://mlsanity.com/paper/2603.14666/) (2026)

## Abstract (arXiv)

Unsupervised domain adaptive segmentation typically relies on self-training using pseudo labels predicted by a pre-trained network on an unlabeled target dataset. However, the noisy nature of such pseudo-labels presents a major bottleneck in adapting a network to the distribution shift between source and target datasets. This challenge is exaggerated when the network encounters an incoming data stream in online fashion, where the network is constrained to adapt to incoming streams of target domain data in exactly one round of forward and backward passes. In this scenario, relying solely on inaccurate pseudo-labels can lead to low-quality segmentation, which is detrimental to medical image analysis where accuracy and precision are of utmost priority. We hypothesize that a small amount of pixel-level annotation obtained from an expert can address this problem, thereby enhancing the performance of domain adaptation of online streaming data, even in the absence of dedicated training data. We call our method ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation that adapts to each incoming data batch in an online setup, incorporating feedback from an expert through active learning. Through active learning, the most informative pixels in each image can be selected for expert annotation. However, the acquisition of pixel-level annotations across all images in a batch often leads to redundant information while increasing temporal overhead in online learning. To reduce the annotation acquisition time and make the adaptation process more online-friendly, we further propose a novel image-pruning strategy that selects the most useful subset of images from the current batch for active learning. Our proposed approach outperforms existing online adaptation approaches and produces competitive results compared to offline domain adaptive active learning methods.