Low-Resource Adversarial Domain Adaptation for Cross-modality Nucleus Detection

Xing, Fuyong; Cornish, Toby C.

doi:10.1007/978-3-031-16449-1_61

Cited by 5 publications

(1 citation statement)

References 31 publications

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“…This is attributed to a large semantic gap between these two domains. Unsupervised Domain Adaptation (UDA) has lately been investigated to bridge this semantic gap between labeled source domain, and unlabeled target domain [29], including adversarial learning for aligning latent representations [25], image translation networks [26], etc. However, these methods produce subpar performance because of the lack of supervision from the target domain and a large semantic gap in style and content information between the source and target domains.…”

Section: Introductionmentioning

confidence: 99%

Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation

Basak,

Yin

2023

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Although unsupervised domain adaptation (UDA) is a promising direction to alleviate domain shift, they fall short of their supervised counterparts. In this work, we investigate relatively less explored semisupervised domain adaptation (SSDA) for medical image segmentation, where access to a few labeled target samples can improve the adaptation performance substantially. Specifically, we propose a two-stage training process. First, an encoder is pre-trained in a self-learning paradigm using a novel domain-content disentangled contrastive learning (CL) along with a pixel-level feature consistency constraint. The proposed CL enforces the encoder to learn discriminative content-specific but domaininvariant semantics on a global scale from the source and target images, whereas consistency regularization enforces the mining of local pixel-level information by maintaining spatial sensitivity. This pre-trained encoder, along with a decoder, is further fine-tuned for the downstream task, (i.e. pixel-level segmentation) using a semi-supervised setting. Furthermore, we experimentally validate that our proposed method can easily be extended for UDA settings, adding to the superiority of the proposed strategy. Upon evaluation on two domain adaptive image segmentation tasks, our proposed method outperforms the SoTA methods, both in SSDA and UDA settings. Code is available at GitHub.

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Section: Introductionmentioning

confidence: 99%