Classification of visibility in multi-stain microscopy imagesDownload PDF

20 Apr 2022, 08:57 (modified: 04 Jun 2022, 12:07)MIDL 2022 Short PapersReaders: Everyone
Keywords: mitotic figure classification, digital pathology, deep learning, dual input networks, attention, immunohistochemical staining
TL;DR: Attention-based method for the classification of visibility of mitotic figures in multi-stain microscopy images.
Abstract: Annotating mitotic figures (MF) in hematoxylin and eosin (H&E) stained slides is an error- prone task that can lead to low inter-rater concordance. Immunohistochemical staining against phospho-histone H3 (PHH3) can lead to higher concordance but, at the same time, to generally higher mitotic figure counts. By annotating MF in PHH3-stained specimen and transferring them to an H&E- re-stained version of the same slide, the high specificity of PHH3 can be used to create high-quality data sets for H&E images. Since considerably more MF can be recognized only in PHH3, this in turn leads to the introduction of label noise. To overcome this problem, we present an attention-based dual-stain classifier which is designed to discriminate MF based on their visibility in H&E. Additionally, we present a data augmentation approach that focuses especially on presenting a large variability of cell pairs to the attention network. The combination of the two methods leads to a weighted accuracy of 0.740 in discriminating H&E-identifiable from non-identifiable MF. Therefore, by automatically discriminating the visibility of MF in H&E slides, PHH3-guided annotation can be used to generate a more reliable ground truth for MF in H&E.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Application: Histopathology
Secondary Subject Area: Detection and Diagnosis
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