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.

采用强化学习模型 RL4Seg，克服医学图像领域域适应性问题，实现准确率 99%，生成具有解剖学可行性的分割图像。

超声心动图分割的域自适应方法基于强化学习

Domain Adaptation of Echocardiography Segmentation Via Reinforcement  Learning

Most uses of Meta-Learning in visual recognition are very often applied to
image classification, with a relative lack of works in other tasks {such} as
segmentation and detection. We propose a generic Meta-Learning framework for
few-shot weakly-supervised segmentation in medical imaging domains. We conduct
a comparative analysis of meta-learners from distinct paradigms adapted to
few-shot image segmentation in different sparsely annotated radiological tasks.
The imaging modalities include 2D chest, mammographic and dental X-rays, as
well as 2D slices of volumetric tomography and resonance images. Our
experiments consider a total of 9 meta-learners, 4 backbones and multiple
target organ segmentation tasks. We explore small-data scenarios in radiology
with varying weak annotation styles and densities. Our analysis shows that
metric-based meta-learning approaches achieve better segmentation results in
tasks with smaller domain shifts in comparison to the meta-training datasets,
while some gradient- and fusion-based meta-learners are more generalizable to
larger domain shifts.

本文提出了一个基于元学习的弱监督分割方案，并在医学成像领域进行了实验和对不同范例的比较分析，研究结果表明度量学习元学习方法在小型领域变化的任务中具有更好的分割结果，而某些梯度和融合的元学习方法则对大型领域变化具有更好的推广性。