We introduce a deep learning-based approach utilizing pyramid sampling for the automated classification of HER2 status in immunohistochemically (IHC) stained breast cancer tissue images. Our deep learning-based method leverages pyramid sampling to analyze features across multiple scales from IHC-stained breast tissue images, managing the computational load effectively and addressing the challenges of HER2 expression heterogeneity by capturing detailed cellular features and broader tissue architecture. Upon application to 523 core images, the model achieved a classification accuracy of 85.47%, demonstrating the ability to counteract staining variability and tissue heterogeneity, which might improve the accuracy and timeliness of breast cancer treatment planning.
The traditional histochemical staining of autopsy tissue samples usually suffers from staining artifacts due to autolysis caused by delayed fixation of cadaver tissues. Here, we introduce an autopsy virtual staining technique to digitally convert autofluorescence images of unlabeled autopsy tissue sections into their hematoxylin and eosin (H&E) stained counterparts through a trained neural network. This technique was demonstrated to effectively mitigate autolysis-induced artifacts inherent in histochemical staining, such as weak nuclear contrast and color fading in the cytoplasmic-extracellular matrix. As a rapid, reagent-efficient, and high-quality histological staining approach, the presented technique holds great potential for widespread application in the future.
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