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Lucas W Remedios1, Shunxing Bao2, Samuel W Remedios3,4

  • 1Vanderbilt University, Department of Computer Science, Nashville, USA.

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Summary
This summary is machine-generated.

This study introduces inter-modality learning to identify more cell types in virtual Hematoxylin and eosin (H&E) stains, advancing digital pathology annotations for better physiological understanding.

Keywords:
H&EMxIFannotationclassificationnuclei classificationstyle transfervirtual H&Ewhole slide imaging

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Area of Science:

  • Digital pathology
  • Computational biology
  • Histology imaging analysis

Background:

  • Cellular communication and spatial relationships are crucial for human physiology.
  • Hematoxylin and eosin (H&E) staining is a common method in clinical and research settings.
  • Current AI models like the Colon Nucleus Identification and Classification (CoNIC) Challenge can only label a limited number of cell types on H&E stains.

Purpose of the Study:

  • To develop a novel method for labeling previously un-labelable cell types on virtual H&E images.
  • To leverage inter-modality learning by combining multiplexed immunofluorescence (MxIF) data with H&E.
  • To enhance the granularity of cell type classification in digital pathology.

Main Methods:

  • Utilized multiplexed immunofluorescence (MxIF) histology imaging to identify 14 distinct cell subclasses.
  • Employed style transfer techniques to generate virtual H&E images from MxIF data.
  • Transferred detailed cell labels from MxIF to the synthesized virtual H&E images for analysis.

Main Results:

  • Successfully identified helper T and progenitor cell nuclei on virtual H&E images.
  • Achieved positive predictive values of 0.34 ± 0.15 for helper T cells and 0.47 ± 0.1 for progenitor cells.
  • Demonstrated the feasibility of transferring high-density labels from MxIF to virtual H&E using inter-modality learning.

Conclusions:

  • Inter-modality learning enables the annotation of a wider range of cell types on virtual H&E images.
  • This approach significantly expands the potential of AI in automating cell classification for digital pathology.
  • The findings represent a promising advancement for detailed cellular analysis in histopathology.