AI Medical Compendium Journal:
Micron (Oxford, England : 1993)

Showing 1 to 10 of 10 articles

Recent developments in denoising medical images using deep learning: An overview of models, techniques, and challenges.

Micron (Oxford, England : 1993)
Medical imaging plays a critical role in diagnosing and treating various medical conditions. However, interpreting medical images can be challenging even for expert clinicians, as they are often degraded by noise and artifacts that can hinder the acc...

Uncovering hidden treasures: Mapping morphological changes in the differentiation of human mesenchymal stem cells to osteoblasts using deep learning.

Micron (Oxford, England : 1993)
Deep Learning (DL) is becoming an increasingly popular technology being employed in life sciences research due to its ability to perform complex and time-consuming tasks with significantly greater speed, accuracy, and reproducibility than human resea...

Quantification of golgi dispersal and classification using machine learning models.

Micron (Oxford, England : 1993)
The Golgi body is a critical organelle in eukaryotic cells responsible for processing and modifying proteins and lipids. Under certain conditions, such as stress, disease, or ageing, the Golgi structure alters. Therefore, understanding the mechanisms...

Recent developments in cervical cancer diagnosis using deep learning on whole slide images: An Overview of models, techniques, challenges and future directions.

Micron (Oxford, England : 1993)
Integration of whole slide imaging (WSI) and deep learning technology has led to significant improvements in the screening and diagnosis of cervical cancer. WSI enables the examination of all cells on a slide simultaneously and deep learning algorith...

Deep learning-based classification of microalgae using light and scanning electron microscopy images.

Micron (Oxford, England : 1993)
Microalgae possess diverse applications, such as food production, animal feed, cosmetics, plastics manufacturing, and renewable energy sources. However, uncontrolled proliferation, known as algal bloom, can detrimentally impact ecosystems. Therefore,...

Comparative study of deep learning algorithms for atomic force microscopy image denoising.

Micron (Oxford, England : 1993)
Atomic force microscopy (AFM) enables direct visualisation of surface topography at the nanoscale. However, post-processing is generally required to obtain accurate, precise, and reliable AFM images owing to the presence of image artefacts. In this s...

Discrete protein metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions.

Micron (Oxford, England : 1993)
Understanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep...

Learning-based defect recognition for quasi-periodic HRSTEM images.

Micron (Oxford, England : 1993)
Controlling crystalline material defects is crucial, as they affect properties of the material that may be detrimental or beneficial for the final performance of a device. Defect analysis on the sub-nanometer scale is enabled by high-resolution scann...

Image reconstruction for sub-sampled atomic force microscopy images using deep neural networks.

Micron (Oxford, England : 1993)
Undersampling is a simple but efficient way to increase the imaging rate of atomic force microscopy (AFM). One major challenge in this approach is that of accurate image reconstruction from a limited number of measurements. In this work, we present a...

Healthy and unhealthy red blood cell detection in human blood smears using neural networks.

Micron (Oxford, England : 1993)
One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on measuring the level of haemoglobin and the classificati...