Low-Resource Adversarial Domain Adaptation for Cross-Modality Nucleus Detection.

ABSTRACT: 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 cas

SUBMITTER: Xing F 

PROVIDER: S-EPMC9648428 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature