VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
Incorporating the image formation process into deep learning improves network performance
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
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.
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.
citing papers explorer
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VitaminP: cross-modal learning enables whole-cell segmentation from routine histology
VitaminP uses paired H&E-mIF data to train a model that transfers molecular boundary information, enabling accurate whole-cell segmentation directly from routine H&E histology across 34 cancer types.
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Scalable Exact Hierarchical Agglomerative Clustering via Sparse Geographic Distance Graphs
GSHAC performs exact HAC on large geographic point sets by building a sparse geodesic graph and proving that connected-component subproblems yield identical results to the dense algorithm for all standard linkages at cut heights below the sparsity threshold.
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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.
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MicroDiffuse3D: A Foundation Model for 3D Microscopy Imaging Restoration
MicroDiffuse3D is a foundation model that restores 3D microscopy images under sparse super-resolution, joint degradation, and low-SNR denoising, reporting 10.58% segmentation and 15.59% line-profile gains over baselines.