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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4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
years
2026 4verdicts
UNVERDICTED 4representative citing papers
Spatial transcriptomics provides cell-type labels and nuclear masks to train image-based deep learning models for nuclei analysis, achieving better segmentation accuracy and transferability to unseen organs than conventional supervised approaches.
Biological spatial priors based on MSI histology, when injected into TransMIL with foundation model features, improve cross-site generalization for MSI prediction from H&E WSIs, with peripheral distance encoding achieving high internal AUC and perfect external specificity.
OpenTME provides pre-computed TME profiles with over 4,500 quantitative readouts per slide from 3,634 TCGA H&E images using an AI pipeline based on pathology foundation models.
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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Leveraging Spatial Transcriptomics as Alternative to Manual Annotations for Deep Learning-Based Nuclei Analysis
Spatial transcriptomics provides cell-type labels and nuclear masks to train image-based deep learning models for nuclei analysis, achieving better segmentation accuracy and transferability to unseen organs than conventional supervised approaches.
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Biological Spatial Priors Regularize Foundation Model Representations for Cross-Site MSI Generalization in Colorectal Cancer
Biological spatial priors based on MSI histology, when injected into TransMIL with foundation model features, improve cross-site generalization for MSI prediction from H&E WSIs, with peripheral distance encoding achieving high internal AUC and perfect external specificity.
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OpenTME: An Open Dataset of AI-powered H&E Tumor Microenvironment Profiles from TCGA
OpenTME provides pre-computed TME profiles with over 4,500 quantitative readouts per slide from 3,634 TCGA H&E images using an AI pipeline based on pathology foundation models.