AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Staining and Labeling

Showing 31 to 40 of 144 articles

Clear Filters

[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.

CellViT: Vision Transformers for precise cell segmentation and classification.

Medical image analysis
Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapp...

NuInsSeg: A fully annotated dataset for nuclei instance segmentation in H&E-stained histological images.

Scientific data
In computational pathology, automatic nuclei instance segmentation plays an essential role in whole slide image analysis. While many computerized approaches have been proposed for this task, supervised deep learning (DL) methods have shown superior s...

RegWSI: Whole slide image registration using combined deep feature- and intensity-based methods: Winner of the ACROBAT 2023 challenge.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful t...

Using machine learning for chemical-free histological tissue staining.

Journal of histotechnology
Hematoxylin and eosin staining can be hazardous, expensive, and prone to error and variability. To circumvent these issues, artificial intelligence/machine learning models such as generative adversarial networks (GANs), are being used to 'virtually' ...

Combining Artificial Intelligence and Simplified Image Processing for the Automatic Detection of Mycobacterium tuberculosis in Acid-fast Stain : A Cross-institute Training and Validation Study.

The American journal of surgical pathology
Tuberculosis (TB) poses a significant health threat in Taiwan, necessitating efficient detection methods. Traditional screening for acid-fast positive bacilli in acid-fast stain is time-consuming and prone to human error due to staining artifacts. To...

Registered multi-device/staining histology image dataset for domain-agnostic machine learning models.

Scientific data
Variations in color and texture of histopathology images are caused by differences in staining conditions and imaging devices between hospitals. These biases decrease the robustness of machine learning models exposed to out-of-domain data. To address...

Unsupervised stain augmentation enhanced glomerular instance segmentation on pathology images.

International journal of computer assisted radiology and surgery
PURPOSE: In pathology images, different stains highlight different glomerular structures, so a supervised deep learning-based glomerular instance segmentation model trained on individual stains performs poorly on other stains. However, it is difficul...

Lung Cancer Diagnosis on Virtual Histologically Stained Tissue Using Weakly Supervised Learning.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Lung adenocarcinoma (LUAD) is the most common primary lung cancer and accounts for 40% of all lung cancer cases. The current gold standard for lung cancer analysis is based on the pathologists' interpretation of hematoxylin and eosin (H&E)-stained ti...