The devil is in the details: Whole slide image acquisition and processing for artifacts detection, color variation, and data augmentation: A review
Whole Slide Images (WSI) are widely used in histopathology for research and the diagnosis
of different types of cancer. The preparation and digitization of histological tissues leads to …
of different types of cancer. The preparation and digitization of histological tissues leads to …
[HTML][HTML] Stain normalization methods for histopathology image analysis: A comprehensive review and experimental comparison
MZ Hoque, A Keskinarkaus, P Nyberg, T Seppänen - Information Fusion, 2024 - Elsevier
The advent of whole slide imaging has brought advanced computer-aided diagnosis via
medical imaging and artificial intelligence technologies in digital pathology. The …
medical imaging and artificial intelligence technologies in digital pathology. The …
Structure-preserving color normalization and sparse stain separation for histological images
Staining and scanning of tissue samples for microscopic examination is fraught with
undesirable color variations arising from differences in raw materials and manufacturing …
undesirable color variations arising from differences in raw materials and manufacturing …
A deep convolutional neural network for segmenting and classifying epithelial and stromal regions in histopathological images
J Xu, X Luo, G Wang, H Gilmore, A Madabhushi - Neurocomputing, 2016 - Elsevier
Epithelial (EP) and stromal (ST) are two types of tissues in histological images. Automated
segmentation or classification of EP and ST tissues is important when developing …
segmentation or classification of EP and ST tissues is important when developing …
Artificial intelligence predicts immune and inflammatory gene signatures directly from hepatocellular carcinoma histology
Background & Aims Patients with hepatocellular carcinoma (HCC) displaying
overexpression of immune gene signatures are likely to be more sensitive to …
overexpression of immune gene signatures are likely to be more sensitive to …
Adaptive color deconvolution for histological WSI normalization
Abstract Background and Objective Color consistency of histological images is significant for
developing reliable computer-aided diagnosis (CAD) systems. However, the color …
developing reliable computer-aided diagnosis (CAD) systems. However, the color …
Deep learning in digital pathology for personalized treatment plans of cancer patients
Over the past decade, many new cancer treatments have been developed and made
available to patients. However, in most cases, these treatments only benefit a specific …
available to patients. However, in most cases, these treatments only benefit a specific …
Application of non-negative matrix factorization in oncology: one approach for establishing precision medicine
R Hamamoto, K Takasawa, H Machino… - Briefings in …, 2022 - academic.oup.com
The increase in the expectations of artificial intelligence (AI) technology has led to machine
learning technology being actively used in the medical field. Non-negative matrix …
learning technology being actively used in the medical field. Non-negative matrix …
SD-layer: stain deconvolutional layer for CNNs in medical microscopic imaging
Abstract Convolutional Neural Networks (CNNs) are typically trained in the RGB color
space. However, in medical imaging, we believe that pixel stain quantities offer a …
space. However, in medical imaging, we believe that pixel stain quantities offer a …
[HTML][HTML] Blind color deconvolution, normalization, and classification of histological images using general super Gaussian priors and Bayesian inference
F Pérez-Bueno, M Vega, MA Sales… - Computer Methods and …, 2021 - Elsevier
Abstract Background and Objective: Color variations in digital histopathology severely
impact the performance of computer-aided diagnosis systems. They are due to differences in …
impact the performance of computer-aided diagnosis systems. They are due to differences in …