Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology
<|reference_start|>Data-driven Nucleus Subclassification on Colon H&E using Style-transferred Digital Pathology: Understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions. H&E is widely available, however, cell subtyping often requires expert knowledge and the use of specialized stains. To reduce the annotation burden, AI has been proposed for the classification of cells on H&E. For example, the recent Colon Nucleus Identification and Classification (CoNIC) Challenge focused on labeling 6 cell types on H&E of the colon. However, the CoNIC Challenge was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We use inter-modality learning to label previously un-labelable cell types on H&E. We take advantage of multiplexed immunofluorescence (MxIF) histology to label 14 cell subclasses. We performed style transfer on the same MxIF tissues to synthesize realistic virtual H&E which we paired with the MxIF-derived cell subclassification labels. We evaluated the efficacy of using a supervised learning scheme where the input was realistic-quality virtual H&E and the labels were MxIF-derived cell subclasses. We assessed our model on private virtual H&E and public real H&E. On virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of $0.34 \pm 0.15$ (prevalence $0.03 \pm 0.01$) and $0.47 \pm 0.1$ (prevalence $0.07 \pm 0.02$) respectively, when using ground truth centroid information. On real H&E we could classify helper T cells and epithelial progenitors with upper bound positive predictive values of $0.43 \pm 0.03$ (parent class prevalence 0.21) and $0.94 \pm 0.02$ (parent class prevalence 0.49) when using ground truth centroid information. This is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.<|reference_end|>
@article{remedios2024data-driven,
    title={Data-driven Nucleus Subclassification on Colon H&E using
  Style-transferred Digital Pathology},
    author={Lucas W. Remedios, Shunxing Bao, Samuel W. Remedios, Ho Hin Lee, Leon
  Y. Cai, Thomas Li, Ruining Deng, Nancy R. Newlin, Adam M. Saunders, Can Cui,
  Jia Li, Qi Liu, Ken S. Lau, Joseph T. Roland, Mary K Washington, Lori A.
  Coburn, Keith T. Wilson, Yuankai Huo, Bennett A. Landman},
    journal={arXiv preprint arXiv:2407.06116},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.06116},
    primaryClass={eess.IV cs.CV cs.LG}
}