A hybrid CNN-transformer model with multi-task learning achieves 91.3% WBC classification accuracy and 0.72 Pearson correlation for CD16 expression regression from label-free DPC images, augmented by LLM-generated summaries.
arXiv preprint arXiv:2402.06191 (2024)
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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.
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Towards Label-Free Single-Cell Phenotyping Using Multi-Task Learning
A hybrid CNN-transformer model with multi-task learning achieves 91.3% WBC classification accuracy and 0.72 Pearson correlation for CD16 expression regression from label-free DPC images, augmented by LLM-generated summaries.
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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.