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Histocytochemistry

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Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification.

IEEE transactions on medical imaging
Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WSI with a pre-trained patch encoder. The performance of classification relies on both patch-level rep...

Structure Embedded Nucleus Classification for Histopathology Images.

IEEE transactions on medical imaging
Nuclei classification provides valuable information for histopathology image analysis. However, the large variations in the appearance of different nuclei types cause difficulties in identifying nuclei. Most neural network based methods are affected ...

An Automated Framework for Histopathological Nucleus Segmentation With Deep Attention Integrated Networks.

IEEE/ACM transactions on computational biology and bioinformatics
Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanw...

DCNNLFS: A Dilated Convolutional Neural Network With Late Fusion Strategy for Intelligent Classification of Gastric Histopathology Images.

IEEE journal of biomedical and health informatics
Gastric cancer has a high incidence rate, significantly threatening patients' health. Gastric histopathology images can reliably diagnose related diseases. Still, the data volume of histopathology images is too large, making misdiagnosis or missed di...

Sliding Window Optimal Transport for Open World Artifact Detection in Histopathology.

IEEE journal of biomedical and health informatics
Histological images are frequently impaired by local artifacts from scanner malfunctions or iatrogenic processes - caused by preparation - impacting the performance of Deep Learning models. Models often struggle with the slightest out-of-distribution...

The Evolving Role of Artificial Intelligence in Gastrointestinal Histopathology: An Update.

Clinical gastroenterology and hepatology : the official clinical practice journal of the American Gastroenterological Association
Significant advances in artificial intelligence (AI) over the past decade potentially may lead to dramatic effects on clinical practice. Digitized histology represents an area ripe for AI implementation. We describe several current needs within the w...

Geometric-attributes-based segmentation of cortical bone slides using optimized neural networks.

Journal of bone and mineral metabolism
In cortical bone, solid (lamellar and interstitial) matrix occupies space left over by porous microfeatures such as Haversian canals, lacunae, and canaliculi-containing clusters. In this work, pulse-coupled neural networks (PCNN) were used to automat...

Classification of subtypes including LCNEC in lung cancer biopsy slides using convolutional neural network from scratch.

Scientific reports
Identifying the lung carcinoma subtype in small biopsy specimens is an important part of determining a suitable treatment plan but is often challenging without the help of special and/or immunohistochemical stains. Pathology image analysis that tackl...

GestAltNet: aggregation and attention to improve deep learning of gestational age from placental whole-slide images.

Laboratory investigation; a journal of technical methods and pathology
The placenta is the first organ to form and performs the functions of the lung, gut, kidney, and endocrine systems. Abnormalities in the placenta cause or reflect most abnormalities in gestation and can have life-long consequences for the mother and ...

Learning from crowds in digital pathology using scalable variational Gaussian processes.

Scientific reports
The volume of labeled data is often the primary determinant of success in developing machine learning algorithms. This has increased interest in methods for leveraging crowds to scale data labeling efforts, and methods to learn from noisy crowd-sourc...