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Histological Techniques

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From Scope to Screen: The Evolution of Histology Education.

Advances in experimental medicine and biology
Histology, the branch of anatomy also known as microscopic anatomy, is the study of the structure and function of the body's tissues. To gain an understanding of the tissues of the body is to learn the foundational underpinnings of anatomy and achiev...

Histologic tissue components provide major cues for machine learning-based prostate cancer detection and grading on prostatectomy specimens.

Scientific reports
Automatically detecting and grading cancerous regions on radical prostatectomy (RP) sections facilitates graphical and quantitative pathology reporting, potentially benefitting post-surgery prognosis, recurrence prediction, and treatment planning aft...

NuClick: A deep learning framework for interactive segmentation of microscopic images.

Medical image analysis
Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive because ...

Deep neural network models for computational histopathology: A survey.

Medical image analysis
Histopathological images contain rich phenotypic information that can be used to monitor underlying mechanisms contributing to disease progression and patient survival outcomes. Recently, deep learning has become the mainstream methodological choice ...

DeepHistReg: Unsupervised Deep Learning Registration Framework for Differently Stained Histology Samples.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The use of several stains during histology sample preparation can be useful for fusing complementary information about different tissue structures. It reveals distinct tissue properties that combined may be useful for gradin...

Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images.

IEEE transactions on medical imaging
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural Networks (CNNs), resulting in ...

Single image super-resolution for whole slide image using convolutional neural networks and self-supervised color normalization.

Medical image analysis
High-quality whole slide scanners used for animal and human pathology scanning are expensive and can produce massive datasets, which limits the access to and adoption of this technique. As a potential solution to these challenges, we present a deep l...

Biomarker-Based Classification and Localization of Renal Lesions Using Learned Representations of Histology-A Machine Learning Approach to Histopathology.

Toxicologic pathology
Several deep learning approaches have been proposed to address the challenges in computational pathology by learning structural details in an unbiased way. Transfer learning allows starting from a learned representation of a pretrained model to be di...

Deep Learning-Based Annotation Transfer between Molecular Imaging Modalities: An Automated Workflow for Multimodal Data Integration.

Analytical chemistry
An ever-increasing array of imaging technologies are being used in the study of complex biological samples, each of which provides complementary, occasionally overlapping information at different length scales and spatial resolutions. It is important...

Generalized Fixation Invariant Nuclei Detection Through Domain Adaptation Based Deep Learning.

IEEE journal of biomedical and health informatics
Nucleus detection is a fundamental task in histological image analysis and an important tool for many follow up analyses. It is known that sample preparation and scanning procedure of histological slides introduce a great amount of variability to the...