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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...

Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images.

Journal of biomedical optics
We study a problem scenario of super-resolution (SR) algorithms in the context of whole slide imaging (WSI), a popular imaging modality in digital pathology. Instead of just one pair of high- and low-resolution images, which is typically the setup in...

Do You Need Embeddings Trained on a Massive Specialized Corpus for Your Clinical Natural Language Processing Task?

Studies in health technology and informatics
We explore the impact of data source on word representations for different NLP tasks in the clinical domain in French (natural language understanding and text classification). We compared word embeddings (Fasttext) and language models (ELMo), learned...

[Recent Developments in a Automated Diagnosis of Pathological Images and Three-dimensional Histopathology].

Brain and nerve = Shinkei kenkyu no shinpo
Rapid improvements in computing power are advancing machine learning technology using neural networks, revolutionizing the field of image analysis and allowing for automated diagnosis of pathological images. In addition, the recent development of tis...

Segmentation of Nuclei in Histopathology Images by Deep Regression of the Distance Map.

IEEE transactions on medical imaging
The advent of digital pathology provides us with the challenging opportunity to automatically analyze whole slides of diseased tissue in order to derive quantitative profiles that can be used for diagnosis and prognosis tasks. In particular, for the ...

Patch-level Tumor Classification in Digital Histopathology Images with Domain Adapted Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Tumor histopathology is a crucial step in cancer diagnosis which involves visual inspection of imaging data to detect the presence of tumor cells among healthy tissues. This manual process can be time-consuming, error-prone, and influenced by the exp...

Deep Learning Models Differentiate Tumor Grades from H&E Stained Histology Sections.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Aberration in tissue architecture is an essential index for cancer diagnosis and tumor grading. Therefore, extracting features of aberrant phenotypes and classification of the histology tissue can provide a model for computer-aided pathology (CAP). A...

Lymph Node Metastasis Status in Breast Carcinoma Can Be Predicted via Image Analysis of Tumor Histology.

Analytical and quantitative cytopathology and histopathology
OBJECTIVE: To develop a method whereby axillary lymph node (ALN) metastasis can be predicted without ALN dissection, via computational image analysis of routinely acquired tumor histology.