A convergent dictionary learning method with TV and non-negativity constraints achieves 94-97% reconstruction fidelity on multi-channel microscopy data and enables unsupervised lymphoid-myeloid cell separation.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
math.NA 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Learned Dictionaries with Total Variation and Non-Negativity for Single-Cell Microscopy: Convergence Theory and Deterministic Multi-Channel Cell Feature Unification
A convergent dictionary learning method with TV and non-negativity constraints achieves 94-97% reconstruction fidelity on multi-channel microscopy data and enables unsupervised lymphoid-myeloid cell separation.