AIMC Topic: Staining and Labeling
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Multivariate and Machine Learning-Derived Virtual Staining and Biochemical Quantification of Cancer Cells through Raman Hyperspectral Imaging.
Analytical chemistry
Jun 11, 2025
Advances in virtual staining and spatial omics have revolutionized our ability to explore cellular architecture and molecular composition with unprecedented detail. Virtual staining techniques, which rely on computational algorithms to map molecular ...
Pixel super-resolved virtual staining of label-free tissue using diffusion models.
Nature communications
May 30, 2025
Virtual staining of tissue offers a powerful tool for transforming label-free microscopy images of unstained tissue into equivalents of histochemically stained samples. This study presents a diffusion model-based pixel super-resolution virtual staini...
A Benchmark for Virus Infection Reporter Virtual Staining in Fluorescence and Brightfield Microscopy.
Scientific data
May 28, 2025
Detecting virus-infected cells in light microscopy requires a reporter signal commonly achieved by immunohistochemistry or genetic engineering. While classification-based machine learning approaches to the detection of virus-infected cells have been ...
Revealing 3D microanatomical structures of unlabeled thick cancer tissues using holotomography and virtual H&E staining.
Nature communications
May 22, 2025
In histopathology, acquiring subcellular-level three-dimensional (3D) tissue structures efficiently and without damaging the tissues during serial sectioning and staining remains a formidable challenge. We address this by integrating holotomography w...
Machine learning prediction of HER2-low expression in breast cancers based on hematoxylin-eosin-stained slides.
Breast cancer research : BCR
Apr 18, 2025
BACKGROUND: Treatment with HER2-targeted therapies is recommended for HER2-positive breast cancer patients with HER2 gene amplification or protein overexpression. Interestingly, recent clinical trials of novel HER2-targeted therapies demonstrated pro...
Multiple instance learning-based prediction of programmed death-ligand 1 (PD-L1) expression from hematoxylin and eosin (H&E)-stained histopathological images in breast cancer.
PeerJ
Apr 15, 2025
Programmed death-ligand 1 (PD-L1) is an important biomarker increasingly used as a predictive marker in breast cancer immunotherapy. Immunohistochemical quantification remains the standard method for assessment. However, it presents challenges relate...
ULST: U-shaped LeWin Spectral Transformer for virtual staining of pathological sections.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 28, 2025
At present, pathological section staining faces several challenges, including complex sample preparation and stringent infrastructure requirements. Virtual staining methods utilizing deep neural networks to automatically generate stained images are g...
Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.
Applied immunohistochemistry & molecular morphology : AIMM
Mar 27, 2025
Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histology alone, has been achieved so far. One of the obstacles is the lack of large data sets to properly...
Artificial intelligence-based virtual staining platform for identifying tumor-associated macrophages from hematoxylin and eosin-stained images.
European journal of cancer (Oxford, England : 1990)
Mar 26, 2025
BACKGROUND: Virtual staining is an artificial intelligence-based approach that transforms pathology images between stain types, such as hematoxylin and eosin (H&E) to immunohistochemistry (IHC), providing a tissue-preserving and efficient alternative...
Deeply supervised two stage generative adversarial network for stain normalization.
Scientific reports
Feb 27, 2025
The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learn...
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