Using Deep Learning Models Pretrained by Self-Supervised Learning for Protein Localization
Background: Task-specific microscopy datasets are often small, making it difficult to train deep learning models that learn robust features. While self-supervised learning (SSL) has shown promise through pretraining on large, domain-specific datasets, generalizability across datasets with differing staining protocols and channel configurations remains underexplored. We investigated the generalizability of SSL models pretrained on ImageNet-1k and HPA FOV, evaluating their embeddings on OpenCell with and without fine-tuning, two channel-mismatch strategies, and varying fine-tuning data fractions. We additionally analyzed single-cell embeddings on a labeled OpenCell subset. Result: DINO-base...
| Authors | Ben Isselmann, Dilara Göksu, Heinz Neumann et al. |
|---|---|
| Primary category | cs.CV |
| Published | 13 April 2026 |
| Last updated at source | 2 September 2026 |
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