HASSL: Hierarchy-Aware Self-Supervised Learning Framework for Single Cell Microscopy
Hierarchical structure is common in image data, where fine-grained clusters often merge into larger, coarser semantic groups. In biological cell images, current self-supervised learning models often suppress this hierarchy, as coarse factors such as imaging modality can obscure finer morphological attributes in the latent space. We propose a hierarchy-aware self-supervised training framework to address this problem. Our method combines two components: a distillation framework with a segmentation teacher to improve morphological awareness in the latent space, and a hierarchy-aware contrastive loss based on HDBSCAN to improve decision boundaries between closely related subtypes at different...
| Authors | Julius Riel, Vishwa Mohan Singh, Sai Anirudh Aryasomayajula et al. |
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| Primary category | cs.CV |
| Published | 5 July 2026 |
| Last updated at source | 5 July 2026 |
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