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A Virtual Domain Collaborative Learning Framework for Semi-supervised Microscopic Hyperspectral Image Segmentation
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DOI：10.1007/978-3-032-05325-1_3.png)
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Abstract
En 中文
Microscopic hyperspectral image segmentation faces dual challenges of limited labeled data and insufficient utilization of unlabeled data. However, existing semi-supervised methods often isolate the training processes for labeled and unlabeled data, neglecting their potential synergistic effects. To address this, we propose a semi-supervised method based on Virtual Domain Collaborative Learning (VDCL) to enhance the collaborative learning ability between labeled and unlabeled data and improve the quality of pseudo-labels. Specifically, by combining unlabeled background with labeled foreground and labeled background with unlabeled foreground to construct virtual domain data pairs, we established a collaborative learning bridge between labeled and unlabeled samples. Furthermore, we establish a repository of optimal models and employ an alternating co-training strategy. The current and historically optimal models jointly guide training, and this dynamic framework significantly improves pseudo-labels quality. We have verified the novel semi-supervised segmentation method on the widely-used public microscopic hyperspectral choledoch dataset from Kaggle and the oral squamous cell carcinoma dataset. On these datasets, our method has achieved the state-of-the-art performance. The code is available at https://github. com/Qugeryolo/Virual-Domain.
Keywords:
Virtual domain
Alternate learning
Hyperspectral image
Semi-supervised segmentation
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