Low-Resource Adversarial Domain Adaptation for Cross-Modality Nucleus Detection.
Low-Resource Adversarial Domain Adaptation for Cross-Modality Nucleus Detection.
复制标题
用于跨模态核检测的低资源对抗域适应。
DOI：
10.1007/978-3-031-16449-1_61
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Cornish,TobyC
中科院分区：
文献类型：
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作者：
Xing,Fuyong;Cornish,TobyC
由于域移位，在一个显微镜图像数据集上训练的深度细胞/细胞核检测模型可能不适用于使用不同成像方式获得的其他数据集。基于生成对抗网络（GANs）的无监督域自适应（UDA）最近被用于缩小域间隙，并取得了优异的核检测性能。然而，目前基于gan的UDA模型训练往往需要大量未标注的目标数据，在实际应用中，这些数据的获取成本可能过高。此外，当使用有限的目标训练数据时，这些方法的性能会显著下降。在本文中，我们研究了一个更现实但更具挑战性的UDA场景，其中（未注释的）目标训练数据非常稀缺，这是以前工作中很少探索核检测的低资源情况。具体来说，我们通过利用特定于任务的模型来增强双GAN网络，以补充目标域鉴别器，并促进生成器在有限数据下的学习。该任务模型受跨域预测一致性约束，以促进图像到图像翻译的语义内容保留。接下来，我们将一个随机的、可微的数据增强模块纳入到任务增强GAN网络中，通过减轻判别器过拟合来进一步改进模型训练。该数据增强模块是一个即插即用组件，不需要修改网络架构或丢失功能。我们评估了提出的低资源UDA方法在多个公共跨模态显微镜图像数据集上的核检测。使用目标域中的单个训练图像，我们的方法显着优于最近最先进的UDA方法，并且与使用真实标记目标数据训练的完全监督模型相比，提供非常有竞争力或更好的性能。
Due to domain shifts, deep cell/nucleus detection models trained on one microscopy image dataset might not be applicable to other datasets acquired with different imaging modalities. Unsupervised domain adaptation (UDA) based on generative adversarial networks (GANs) has recently been exploited to close domain gaps and has achieved excellent nucleus detection performance. However, current GAN-based UDA model training often requires a large amount of unannotated target data, which may be prohibitively expensive to obtain in real practice. Additionally, these methods have significant performance degradation when using limited target training data. In this paper, we study a more realistic yet challenging UDA scenario, where (unannotated) target training data is very scarce, a low-resource case rarely explored for nucleus detection in previous work. Specifically, we augment a dual GAN network by leveraging a task-specific model to supplement the target-domain discriminator and facilitate generator learning with limited data. The task model is constrained by cross-domain prediction consistency to encourage semantic content preservation for image-to-image translation. Next, we incorporate a stochastic, differentiable data augmentation module into the task-augmented GAN network to further improve model training by alleviating discriminator overfitting. This data augmentation module is a plug-and-play component, requiring no modification of network architectures or loss functions. We evaluate the proposed low-resource UDA method for nucleus detection on multiple public cross-modality microscopy image datasets. With a single training image in the target domain, our method significantly outperforms recent state-of-the-art UDA approaches and delivers very competitive or superior performance over fully supervised models trained with real labeled target data.