Histological Detection of High-Risk Benign Breast Lesions from Whole Slide Images

Tosun, Akif Burak; Nguyen, Luong Anh Tuan; Ong, Nathan; Navolotskaia, Olga; Carter, Gloria; Fine, Jeffrey L.; Taylor, D. Lansing; Chennubhotla, S. Chakra

doi:10.1007/978-3-319-66185-8_17

Cited by 7 publications

(1 citation statement)

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“…Trahearn et al [20] propose a hyper stain inspector for image alignment and cancer detection as well. Another innovative way of "virtual staining" method is proposed in [21]. The authors represent different tissue components in the image with colourful plates of different size, facilitating diagnosis.…”

Section: Related Workmentioning

confidence: 99%

GAN-based Virtual Re-Staining: A Promising Solution for Whole Slide Image Analysis

Xu¹,

Moro²,

Bozóky³

et al. 2019

Preprint

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Histopathological cancer diagnosis is based on visual examination of stained tissue slides. Hematoxylin and eosin (H&E) is a standard stain routinely employed worldwide. It is easy to acquire and cost effective, but cells and tissue components show low-contrast with varying tones of dark blue and pink, which makes difficult visual assessments, digital image analysis, and quantifications. These limitations can be overcome by IHC staining of target proteins of the tissue slide. IHC provides a selective, high-contrast imaging of cells and tissue components, but their use is largely limited by a significantly more complex laboratory processing and high cost. We proposed a conditional CycleGAN (cC-GAN) network to transform the H&E stained images into IHC stained images, facilitating virtual IHC staining on the same slide. This datadriven method requires only a limited amount of labelled data but will generate pixel level segmentation results. The proposed cCGAN model improves the original network [1] by adding category conditions and introducing two structural loss functions, which realize a multi-subdomain translation and improve the translation accuracy as well. Experiments demonstrate that the proposed model outperforms the original method in unpaired image translation with multi-subdomains. We also explore the potential of unpaired images to image translation method applied on other histology images related tasks with different staining techniques.

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Section: Related Workmentioning

confidence: 99%