Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning

• URL: http://arxiv.org/abs/2406.17902v1
• Date: Tue, 25 Jun 2024 19:26:39 GMT
• Title: Domain Adaptation of Echocardiography Segmentation Via Reinforcement Learning
• Authors: Arnaud Judge, Thierry Judge, Nicolas Duchateau, Roman A. Sandler, Joseph Z. Sokol, Olivier Bernard, Pierre-Marc Jodoin,
• Abstract summary: We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain.
• Score: 4.850478245721347
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Performance of deep learning segmentation models is significantly challenged in its transferability across different medical imaging domains, particularly when aiming to adapt these models to a target domain with insufficient annotated data for effective fine-tuning. While existing domain adaptation (DA) methods propose strategies to alleviate this problem, these methods do not explicitly incorporate human-verified segmentation priors, compromising the potential of a model to produce anatomically plausible segmentations. We introduce RL4Seg, an innovative reinforcement learning framework that reduces the need to otherwise incorporate large expertly annotated datasets in the target domain, and eliminates the need for lengthy manual human review. Using a target dataset of 10,000 unannotated 2D echocardiographic images, RL4Seg not only outperforms existing state-of-the-art DA methods in accuracy but also achieves 99% anatomical validity on a subset of 220 expert-validated subjects from the target domain. Furthermore, our framework's reward network offers uncertainty estimates comparable with dedicated state-of-the-art uncertainty methods, demonstrating the utility and effectiveness of RL4Seg in overcoming domain adaptation challenges in medical image segmentation.

Related papers

• An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation [10.061310311839856]
  Source-free Domain Adaptation (SFDA) is a promising technique to adapt deep segmentation models to address privacy and security concerns. We propose a novel Uncertainty-guided Tiered Self-training (UGTST) framework to achieve stable domain adaptation.
  arXiv  Detail & Related papers  (2024-07-03T08:13:16Z)
• Subject-Based Domain Adaptation for Facial Expression Recognition [51.10374151948157]
  Adapting a deep learning model to a specific target individual is a challenging facial expression recognition task. This paper introduces a new MSDA method for subject-based domain adaptation in FER. It efficiently leverages information from multiple source subjects to adapt a deep FER model to a single target individual.
  arXiv  Detail & Related papers  (2023-12-09T18:40:37Z)
• Domain-Adaptive Learning: Unsupervised Adaptation for Histology Images with Improved Loss Function Combination [3.004632712148892]
  This paper presents a novel approach for unsupervised domain adaptation (UDA) targeting H&E stained histology images. Our approach proposes a novel loss function along with carefully selected existing loss functions tailored to address the challenges specific to histology images. The proposed method is extensively evaluated in accuracy, robustness, and generalization, surpassing state-of-the-art techniques for histology images.
  arXiv  Detail & Related papers  (2023-09-29T12:11:16Z)
• Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals [9.845637899896365]
  Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. We propose a novel Sourece-Free Active Domain Adaptation (SFADA) framework to facilitate domain adaptation for the Gross Tumor Volume (GTV) segmentation task. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach.
  arXiv  Detail & Related papers  (2023-09-23T15:26:27Z)
• Taxonomy Adaptive Cross-Domain Adaptation in Medical Imaging via Optimization Trajectory Distillation [73.83178465971552]
  The success of automated medical image analysis depends on large-scale and expert-annotated training sets. Unsupervised domain adaptation (UDA) has been raised as a promising approach to alleviate the burden of labeled data collection. We propose optimization trajectory distillation, a unified approach to address the two technical challenges from a new perspective.
  arXiv  Detail & Related papers  (2023-07-27T08:58:05Z)
• Unsupervised Domain Adaptation for Low-dose CT Reconstruction via Bayesian Uncertainty Alignment [32.632944734192435]
  Low-dose computed tomography (LDCT) image reconstruction techniques can reduce patient radiation exposure while maintaining acceptable imaging quality. Deep learning is widely used in this problem, but the performance of testing data is often degraded in clinical scenarios. Unsupervised domain adaptation (UDA) of LDCT reconstruction has been proposed to solve this problem through distribution alignment.
  arXiv  Detail & Related papers  (2023-02-26T07:10:09Z)
• Anatomy-guided domain adaptation for 3D in-bed human pose estimation [62.3463429269385]
  3D human pose estimation is a key component of clinical monitoring systems. We present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain. Our method consistently outperforms various state-of-the-art domain adaptation methods.
  arXiv  Detail & Related papers  (2022-11-22T11:34:51Z)
• Stagewise Unsupervised Domain Adaptation with Adversarial Self-Training for Road Segmentation of Remote Sensing Images [93.50240389540252]
  Road segmentation from remote sensing images is a challenging task with wide ranges of application potentials. We propose a novel stagewise domain adaptation model called RoadDA to address the domain shift (DS) issue in this field. Experiment results on two benchmarks demonstrate that RoadDA can efficiently reduce the domain gap and outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-08-28T09:29:14Z)
• Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation [61.01704175938995]
  In this paper, we propose a novel Bidirectional Global-to-Local (BiGL) adaptation framework under a UDA scheme. Specifically, a bidirectional image synthesis and segmentation module is proposed to segment the brain tumor. The proposed method outperforms several state-of-the-art unsupervised domain adaptation methods by a large margin.
  arXiv  Detail & Related papers  (2021-05-17T10:11:45Z)
• Shape-aware Meta-learning for Generalizing Prostate MRI Segmentation to Unseen Domains [68.73614619875814]
  We present a novel shape-aware meta-learning scheme to improve the model generalization in prostate MRI segmentation. Experimental results show that our approach outperforms many state-of-the-art generalization methods consistently across all six settings of unseen domains.
  arXiv  Detail & Related papers  (2020-07-04T07:56:02Z)
• Domain Adaptive Medical Image Segmentation via Adversarial Learning of Disease-Specific Spatial Patterns [6.298270929323396]
  We propose an unsupervised domain adaptation framework for boosting image segmentation performance across multiple domains. We enforce architectures to be adaptive to new data by rejecting improbable segmentation patterns and implicitly learning through semantic and boundary information. We demonstrate that recalibrating the deep networks on a few unlabeled images from the target domain improves the segmentation accuracy significantly.
  arXiv  Detail & Related papers  (2020-01-25T13:48:02Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.