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Staining and Labeling

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RMDL: Recalibrated multi-instance deep learning for whole slide gastric image classification.

Medical image analysis
The whole slide histopathology images (WSIs) play a critical role in gastric cancer diagnosis. However, due to the large scale of WSIs and various sizes of the abnormal area, how to select informative regions and analyze them are quite challenging du...

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology.

Medical image analysis
Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with...

Unsupervised method for normalization of hematoxylin-eosin stain in histological images.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Histological images stained with hematoxylin-eosin are widely used by pathologists for cancer diagnosis. However, these images can have color variations that highly influence the histological image processing techniques. To deal with this potential l...

Automatic recognition of different types of acute leukaemia in peripheral blood by image analysis.

Journal of clinical pathology
AIMS: Morphological differentiation among different blast cell lineages is a difficult task and there is a lack of automated analysers able to recognise these abnormal cells. This study aims to develop a machine learning approach to predict the diagn...

BACH: Grand challenge on breast cancer histology images.

Medical image analysis
Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysi...

Multi-Path Dilated Residual Network for Nuclei Segmentation and Detection.

Cells
As a typical biomedical detection task, nuclei detection has been widely used in human health management, disease diagnosis and other fields. However, the task of cell detection in microscopic images is still challenging because the nuclei are common...

Virtual histological staining of unlabelled tissue-autofluorescence images via deep learning.

Nature biomedical engineering
The histological analysis of tissue samples, widely used for disease diagnosis, involves lengthy and laborious tissue preparation. Here, we show that a convolutional neural network trained using a generative adversarial-network model can transform wi...

High-Quality Immunohistochemical Stains Through Computational Assay Parameter Optimization.

IEEE transactions on bio-medical engineering
Accurate profiling of tumors using immunohistochemistry (IHC) is essential in cancer diagnosis. The inferences drawn from IHC-stained images depend to a great extent on the quality of immunostaining, which is in turn affected strongly by assay parame...

Segmentation of glandular epithelium in colorectal tumours to automatically compartmentalise IHC biomarker quantification: A deep learning approach.

Medical image analysis
In this paper, we propose a method for automatically annotating slide images from colorectal tissue samples. Our objective is to segment glandular epithelium in histological images from tissue slides submitted to different staining techniques, includ...

Quantitative nuclear histomorphometry predicts oncotype DX risk categories for early stage ER+ breast cancer.

BMC cancer
BACKGROUND: Gene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. Howev...