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Staining and Labeling

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Robot-Guided Atomic Force Microscopy for Mechano-Visual Phenotyping of Cancer Specimens.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada
Atomic force microscopy (AFM) and other forms of scanning probe microscopy have been successfully used to assess biomechanical and bioelectrical characteristics of individual cells. When extending such approaches to heterogeneous tissue, there exists...

An unsupervised feature learning framework for basal cell carcinoma image analysis.

Artificial intelligence in medicine
OBJECTIVE: The paper addresses the problem of automatic detection of basal cell carcinoma (BCC) in histopathology images. In particular, it proposes a framework to both, learn the image representation in an unsupervised way and visualize discriminati...

A SERS-Assisted 3D Barcode Chip for High-Throughput Biosensing.

Small (Weinheim an der Bergstrasse, Germany)
A surface enhanced Raman scattering (SERS)-assisted 3D barcode chip has been developed for high-throughput biosensing. The 3D barcode is realized through joint 2D spatial encoding with the Raman spectroscopic encoding, which stores the SERS fingerpri...

A machine learning approach to automate microinfarct and microhemorrhage screening in hematoxylin and eosin-stained human brain tissues.

Journal of neuropathology and experimental neurology
Microinfarcts and microhemorrhages are characteristic lesions of cerebrovascular disease. Although multiple studies have been published, there is no one universal standard criteria for the neuropathological assessment of cerebrovascular disease. In t...

Cancer-Associated Lymphoid Aggregates in Histology Images: Manual and Deep Learning-Based Quantification Approaches.

Methods in molecular biology (Clifton, N.J.)
Quantification of lymphoid aggregates including tertiary lymphoid structures (TLS) with germinal centers in histology images of cancer is a promising approach for developing prognostic and predictive tissue biomarkers. In this article, we provide rec...

High-rate emphasized DeepLabV3Plus for Semantic Segmentation of Breast Cancer-related Hematoxylin and Eosin-stained Images.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Deep learning algorithms have been successfully adopted to extract meaningful information from digital images, yet many of them have been untapped in the semantic image segmentation of histopathology images. In this paper, we propose a deep convoluti...

Staining, magnification, and algorithmic conditions for highly accurate cell detection and cell classification by deep learning.

American journal of clinical pathology
OBJECTIVES: Research into cytodiagnosis has seen an active exploration of cell detection and classification using deep learning models. We aimed to clarify the challenges of magnification, staining methods, and false positives in creating general pur...

PXPermute reveals staining importance in multichannel imaging flow cytometry.

Cell reports methods
Imaging flow cytometry (IFC) allows rapid acquisition of numerous single-cell images per second, capturing information from multiple fluorescent channels. However, the traditional process of staining cells with fluorescently labeled conjugated antibo...

[Comparative analysis of two assaysin detection of sperm DNA fragmentation index, flow cytometry and AI-based fluorescence microscopy, based on AO staining: A multicentre study].

Zhonghua nan ke xue = National journal of andrology
OBJECTIVE: To study the correlation, consistency, and variations between two assays of DNA fragmentation index based on acridine orange (AO) staining via AI-based fluorescence microscopy(AI-DFI), and flow cytometry (FCM-DFI) across multiple centers.

[Application of Deep Learning Algorithm in the Grading Assessment of Corneal Fluorescein Staining].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To explore the application value of applying deep learning (DL) algorithm in the grading assessment of corneal fluorescein staining.