Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection
<|reference_start|>Histology Virtual Staining with Mask-Guided Adversarial Transfer Learning for Tertiary Lymphoid Structure Detection: Histological Tertiary Lymphoid Structures (TLSs) are increasingly recognized for their correlation with the efficacy of immunotherapy in various solid tumors. Traditionally, the identification and characterization of TLSs rely on immunohistochemistry (IHC) staining techniques, utilizing markers such as CD20 for B cells. Despite the specificity of IHC, Hematoxylin-Eosin (H&E) staining offers a more accessible and cost-effective choice. Capitalizing on the prevalence of H&E staining slides, we introduce a novel Mask-Guided Adversarial Transfer Learning method designed for virtual pathological staining. This method adeptly captures the nuanced color variations across diverse tissue types under various staining conditions, such as nucleus, red blood cells, positive reaction regions, without explicit label information, and adeptly synthesizes realistic IHC-like virtual staining patches, even replicating the positive reaction. Further, we propose the Virtual IHC Pathology Analysis Network (VIPA-Net), an integrated framework encompassing a Mask-Guided Transfer Module and an H&E-Based Virtual Staining TLS Detection Module. VIPA-Net synergistically harnesses both H\&E staining slides and the synthesized virtual IHC patches to enhance the detection of TLSs within H&E Whole Slide Images (WSIs). We evaluate the network with a comprehensive dataset comprising 1019 annotated slides from The Cancer Genome Atlas (TCGA). Experimental results compellingly illustrate that the VIPA-Net substantially elevates TLS detection accuracy, effectively circumventing the need for actual CD20 staining across the public dataset.<|reference_end|>
@article{wang2024histology,
    title={Histology Virtual Staining with Mask-Guided Adversarial Transfer
  Learning for Tertiary Lymphoid Structure Detection},
    author={Qiuli Wang, Yongxu Liu, Li Ma, Xianqi Wang, Wei Chen, and Xiaohong Yao},
    journal={arXiv preprint arXiv:2408.13978},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.13978},
    primaryClass={eess.IV cs.CV}
}