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Histological Techniques

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Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks.

Scientific reports
Classification of histologic patterns in lung adenocarcinoma is critical for determining tumor grade and treatment for patients. However, this task is often challenging due to the heterogeneous nature of lung adenocarcinoma and the subjective criteri...

Evaluating reproducibility of AI algorithms in digital pathology with DAPPER.

PLoS computational biology
Artificial Intelligence is exponentially increasing its impact on healthcare. As deep learning is mastering computer vision tasks, its application to digital pathology is natural, with the promise of aiding in routine reporting and standardizing resu...

Deep Convolutional Hashing for Low-Dimensional Binary Embedding of Histopathological Images.

IEEE journal of biomedical and health informatics
Compact binary representations of histopa-thology images using hashing methods provide efficient approximate nearest neighbor search for direct visual query in large-scale databases. They can be utilized to measure the probability of the abnormality ...

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 ...

MILD-Net: Minimal information loss dilated network for gland instance segmentation in colon histology images.

Medical image analysis
The analysis of glandular morphology within colon histopathology images is an important step in determining the grade of colon cancer. Despite the importance of this task, manual segmentation is laborious, time-consuming and can suffer from subjectiv...

Micro-Net: A unified model for segmentation of various objects in microscopy images.

Medical image analysis
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy i...

Rapid histology of laryngeal squamous cell carcinoma with deep-learning based stimulated Raman scattering microscopy.

Theranostics
Maximal resection of tumor while preserving the adjacent healthy tissue is particularly important for larynx surgery, hence precise and rapid intraoperative histology of laryngeal tissue is crucial for providing optimal surgical outcomes. We hypothes...

Transfer learning for classification of cardiovascular tissues in histological images.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Automatic classification of healthy tissues and organs based on histology images is an open problem, mainly due to the lack of automated tools. Solutions in this regard have potential in educational medicine and medical prac...

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...