AIMC Topic: Microscopy

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A deep learning dataset for sample preparation artefacts detection in multispectral high-content microscopy.

Scientific data
High-content image-based screening is widely used in Drug Discovery and Systems Biology. However, sample preparation artefacts may significantly deteriorate the quality of image-based screening assays. While detection and circumvention of such artefa...

Validation of artificial intelligence-based digital microscopy for automated detection of Schistosoma haematobium eggs in urine in Gabon.

PLoS neglected tropical diseases
INTRODUCTION: Schistosomiasis is a significant public health concern, especially in Sub-Saharan Africa. Conventional microscopy is the standard diagnostic method in resource-limited settings, but with limitations, such as the need for expert microsco...

Efficient leukocytes detection and classification in microscopic blood images using convolutional neural network coupled with a dual attention network.

Computers in biology and medicine
Leukocytes, also called White Blood Cells (WBCs) or leucocytes, are the cells that play a pivotal role in human health and are vital indicators of diseases such as malaria, leukemia, AIDS, and other viral infections. WBCs detection and classification...

ChatGPT's innovative application in blood morphology recognition.

Journal of the Chinese Medical Association : JCMA
BACKGROUND: Recently, the rapid advancement in generative artificial intelligence (AI) technology, such as ChatGPT-4, has sparked discussions, particularly in image recognition. Accurate results are critical for hematological diagnosis, particularly ...

Assessing Fuchs Corneal Endothelial Dystrophy Using Artificial Intelligence-Derived Morphometric Parameters From Specular Microscopy Images.

Cornea
PURPOSE: The aim of this study was to evaluate the efficacy of artificial intelligence-derived morphometric parameters in characterizing Fuchs corneal endothelial dystrophy (FECD) from specular microscopy images.

A high-speed microscopy system based on deep learning to detect yeast-like fungi cells in blood.

Bioanalysis
Blood-invasive fungal infections can cause the death of patients, while diagnosis of fungal infections is challenging. A high-speed microscopy detection system was constructed that included a microfluidic system, a microscope connected to a high-sp...

Deep Learning-Based Culture-Free Bacteria Detection in Urine Using Large-Volume Microscopy.

Biosensors
Bacterial infections, increasingly resistant to common antibiotics, pose a global health challenge. Traditional diagnostics often depend on slow cell culturing, leading to empirical treatments that accelerate antibiotic resistance. We present a novel...

CellT-Net: A Composite Transformer Method for 2-D Cell Instance Segmentation.

IEEE journal of biomedical and health informatics
Cell instance segmentation (CIS) via light microscopy and artificial intelligence (AI) is essential to cell and gene therapy-based health care management, which offers the hope of revolutionary health care. An effective CIS method can help clinicians...

Current Landscape of Advanced Imaging Tools for Pathology Diagnostics.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
Histopathology relies on century-old workflows of formalin fixation, paraffin embedding, sectioning, and staining tissue specimens on glass slides. Despite being robust, this conventional process is slow, labor-intensive, and limited to providing two...

Protocol to train a support vector machine for the automatic curation of bacterial cell detections in microscopy images.

STAR protocols
Manual curation of bacterial cell detections in microscopy images remains a time-consuming and laborious task. This work offers a comprehensive, step-by-step tutorial on training a support vector machine to autonomously distinguish between good and b...