AI Medical Compendium Topic

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

Cytological Techniques

Showing 11 to 20 of 38 articles

Clear Filters

The emerging role of deep learning in cytology.

Cytopathology : official journal of the British Society for Clinical Cytology
Deep learning (DL) is a component or subset of artificial intelligence. DL has contributed significant change in feature extraction and image classification. Various algorithmic models are used in DL such as a convolutional neural network (CNN), recu...

Probing the characteristics and biofunctional effects of disease-affected cells and drug response via machine learning applications.

Critical reviews in biotechnology
Drug-induced transformations in disease characteristics at the cellular and molecular level offers the opportunity to predict and evaluate the efficacy of pharmaceutical ingredients whilst enabling the optimal design of new and improved drugs with en...

Shape-to-graph mapping method for efficient characterization and classification of complex geometries in biological images.

PLoS computational biology
With the ever-increasing quality and quantity of imaging data in biomedical research comes the demand for computational methodologies that enable efficient and reliable automated extraction of the quantitative information contained within these image...

Deep Learning-Based Quantification of Pulmonary Hemosiderophages in Cytology Slides.

Scientific reports
Exercise-induced pulmonary hemorrhage (EIPH) is a common condition in sport horses with negative impact on performance. Cytology of bronchoalveolar lavage fluid by use of a scoring system is considered the most sensitive diagnostic method. Macrophage...

Comparing convolutional neural networks and preprocessing techniques for HEp-2 cell classification in immunofluorescence images.

Computers in biology and medicine
Autoimmune diseases are the third highest cause of mortality in the world, and the identification of an anti-nuclear antibody via an immunofluorescence test for HEp-2 cells is a standard procedure to support diagnosis. In this work, we assess the per...

Automated Classification of Apoptosis in Phase Contrast Microscopy Using Capsule Network.

IEEE transactions on medical imaging
Automatic and accurate classification of apoptosis, or programmed cell death, will facilitate cell biology research. The state-of-the-art approaches in apoptosis classification use deep convolutional neural networks (CNNs). However, these networks ar...

Blood Cell Classification Based on Hyperspectral Imaging With Modulated Gabor and CNN.

IEEE journal of biomedical and health informatics
Cell classification, especially that of white blood cells, plays a very important role in the field of diagnosis and control of major diseases. Compared to traditional optical microscopic imaging, hyperspectral imagery, combined with both spatial and...

Cell Line Classification Using Electric Cell-Substrate Impedance Sensing (ECIS).

The international journal of biostatistics
We present new methods for cell line classification using multivariate time series bioimpedance data obtained from electric cell-substrate impedance sensing (ECIS) technology. The ECIS technology, which monitors the attachment and spreading of mammal...