Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
U-net: Convolutional networks for biomedical image segmentation
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A multitask learning method with instance-aware pseudo-labeling and class-focused contrastive learning outperforms prior UDA and WDA techniques for weakly supervised domain-adaptive mitochondria segmentation in EM images.
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Prefer-DAS: Learning from Local Preferences and Sparse Prompts for Domain Adaptive Segmentation of Electron Microscopy
Prefer-DAS integrates self-training, prompt-guided contrastive learning, local direct preference optimization (LPO), and unsupervised preference optimization (UPO) to achieve effective domain adaptive segmentation in electron microscopy using sparse prompts and local preferences.
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Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy
A multitask learning method with instance-aware pseudo-labeling and class-focused contrastive learning outperforms prior UDA and WDA techniques for weakly supervised domain-adaptive mitochondria segmentation in EM images.