Recent advances of deep learning for computational histopathology: principles and applications
Simple Summary The histopathological image is widely considered as the gold standard for
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
the diagnosis and prognosis of human cancers. Recently, deep learning technology has …
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 …
Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review
Breast cancer in women is the second most common cancer worldwide. Early detection of
breast cancer can reduce the risk of human life. Non-invasive techniques such as …
breast cancer can reduce the risk of human life. Non-invasive techniques such as …
The utility of color normalization for AI‐based diagnosis of hematoxylin and eosin‐stained pathology images
J Boschman, H Farahani, A Darbandsari… - The Journal of …, 2022 - Wiley Online Library
The color variation of hematoxylin and eosin (H&E)‐stained tissues has presented a
challenge for applications of artificial intelligence (AI) in digital pathology. Many color …
challenge for applications of artificial intelligence (AI) in digital pathology. Many color …
Cervical cancer metastasis and recurrence risk prediction based on deep convolutional neural network
Z Ye, Y Zhang, Y Liang, J Lang, X Zhang… - Current …, 2022 - ingentaconnect.com
Background: Evaluating the risk of metastasis and recurrence of a cervical cancer patient is
critical for appropriate adjuvant therapy. However, current risk assessment models usually …
critical for appropriate adjuvant therapy. However, current risk assessment models usually …
Randstainna: Learning stain-agnostic features from histology slides by bridging stain augmentation and normalization
Stain variations often decrease the generalization ability of deep learning based
approaches in digital histopathology analysis. Two separate proposals, namely stain …
approaches in digital histopathology analysis. Two separate proposals, namely stain …
Stain transfer using generative adversarial networks and disentangled features
AZ Moghadam, H Azarnoush, SA Seyyedsalehi… - Computers in Biology …, 2022 - Elsevier
With the digitization of histopathology, machine learning algorithms have been developed to
help pathologists. Color variation in histopathology images degrades the performance of …
help pathologists. Color variation in histopathology images degrades the performance of …
The digital brain tumour atlas, an open histopathology resource
T Roetzer-Pejrimovsky, AC Moser, B Atli, CC Vogel… - Scientific Data, 2022 - nature.com
Currently, approximately 150 different brain tumour types are defined by the WHO. Recent
endeavours to exploit machine learning and deep learning methods for supporting more …
endeavours to exploit machine learning and deep learning methods for supporting more …
Histopathology stain-color normalization using deep generative models
Performance of designed CAD algorithms for histopathology image analysis is affected by
the amount of variations in the samples such as color and intensity of stained images. Stain …
the amount of variations in the samples such as color and intensity of stained images. Stain …
Quantifying the effect of color processing on blood and damaged tissue detection in whole slide images
Histological tissue examination has been a longstanding practice for cancer diagnosis
where pathologists identify the presence of tumors on glass slides. Slides acquired from …
where pathologists identify the presence of tumors on glass slides. Slides acquired from …