Laryngeal Cancer Lesion Segmentation in P63 Immunohistochemically Stained Histology Images

Ahmad, Ibtihaj; Islam, Zain Ul

doi:10.1109/biosmart54244.2021.9677811

Cited by 3 publications

(1 citation statement)

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“…In certain cancers, like metaplastic breast cancer, the p63 immunohistochemical stain becomes vital for identifying myoepithelial cell invasion, a marker of cancer [4]. The combination of H&E and p63 stains has been shown to enhance the accuracy of cancer diagnoses, including in breast and laryngeal cancers [5,6], aiding in differentiating invasive carcinoma from benign lesions.…”

Section: Introduction 1backgroundmentioning

confidence: 99%

Attention-Enhanced Unpaired xAI-GANs for Transformation of Histological Stain Images

Sloboda,

Hudec,

Halinkovič

et al. 2024

J. Imaging

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Histological staining is the primary method for confirming cancer diagnoses, but certain types, such as p63 staining, can be expensive and potentially damaging to tissues. In our research, we innovate by generating p63-stained images from H&E-stained slides for metaplastic breast cancer. This is a crucial development, considering the high costs and tissue risks associated with direct p63 staining. Our approach employs an advanced CycleGAN architecture, xAI-CycleGAN, enhanced with context-based loss to maintain structural integrity. The inclusion of convolutional attention in our model distinguishes between structural and color details more effectively, thus significantly enhancing the visual quality of the results. This approach shows a marked improvement over the base xAI-CycleGAN and standard CycleGAN models, offering the benefits of a more compact network and faster training even with the inclusion of attention.

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Section: Introduction 1backgroundmentioning

confidence: 99%