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Research questionHow can self-supervised representations transfer reliably across microscopy datasets with scarce labels and mismatched staining or channels?Microscopy tasks often have too few labeled images to learn robust features from scratch, while staining protocols and available channels vary between datasets. These differences make it unclear when representations learned from one imaging collection remain useful for protein localization in another.
AI
Computer Vision
Image & Video Processing
Machine Learning
Research Paper
Latest papersRecent research connected to this question, newest first.Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein LocalizationThe evidence concerns DINO-based Vision Transformer backbones pretrained on ImageNet-1k or Human Protein Atlas field-of-view and single-cell data, then transferred to OpenCell protein-localization tasks with and without fine-tuning. It covers channel-mismatch strategies, varying fine-tuning data fractions, multiclass labels, and single-cell k-nearest-neighbor evaluation; it does not establish transfer to other imaging tasks or datasets.research paper · Sep 2, 2026
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