AIMC Topic: Microscopy
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A novel approach in cancer diagnosis: integrating holography microscopic medical imaging and deep learning techniques-challenges and future trends.
Biomedical physics & engineering express
Jan 29, 2025
Medical imaging is pivotal in early disease diagnosis, providing essential insights that enable timely and accurate detection of health anomalies. Traditional imaging techniques, such as Magnetic Resonance Imaging (MRI), Computer Tomography (CT), ult...
VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic Resonance Imaging (MMRI).
Microscopy research and technique
Jan 17, 2025
The development of deep learning algorithms has transformed medical image analysis, especially in brain tumor recognition. This research introduces a robust automatic microbrain tumor identification method utilizing the VGG16 deep learning model. Mic...
Representative training data sets are critical for accurate machine-learning classification of microscopy images of particles formed by lipase-catalyzed polysorbate hydrolysis.
Journal of pharmaceutical sciences
Jan 15, 2025
Polysorbate 20 (PS20) is commonly used as an excipient in therapeutic protein formulations. However, over the course of a therapeutic protein product's shelf life, minute amounts of co-purified host-cell lipases may cause slow hydrolysis of PS20, rel...
Image-based fuzzy logic control for pressure-driven droplet microfluidics as autosampler for multimodal imaging microscopy.
Lab on a chip
Jan 14, 2025
Here we present a highly customisable image-based fuzzy logic control (FLC) method for pressure-driven droplet microfluidics. The system is designed to position droplets of different sizes in microfluidic chips of varying channel size in the centre o...
Virtual Gram staining of label-free bacteria using dark-field microscopy and deep learning.
Science advances
Jan 8, 2025
Gram staining has been a frequently used staining protocol in microbiology. It is vulnerable to staining artifacts due to, e.g., operator errors and chemical variations. Here, we introduce virtual Gram staining of label-free bacteria using a trained ...
VoxelMorph-Based Deep Learning Motion Correction for Ultrasound Localization Microscopy of Spinal Cord.
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Jan 8, 2025
Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization microscopy (ULM) offers super-resolution imaging of microvasculature by localizing and tracking individua...
Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.
International journal of environmental research and public health
Dec 31, 2024
The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital ima...
Deep learning enabled rapid classification of yeast species in food by imaging of yeast microcolonies.
Food research international (Ottawa, Ont.)
Dec 30, 2024
Diverse species of yeasts are commonly associated with food and food production environments. The contamination of food products by spoilage yeasts poses significant challenges, leading to quality degradation and food loss. Similarly, the introductio...
Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.
Nature communications
Dec 30, 2024
Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make the spatial resolution of photoacoustic microscopy significantly poorer than that of conventional conf...
Enhanced Detection of Leishmania Parasites in Microscopic Images Using Machine Learning Models.
Sensors (Basel, Switzerland)
Dec 21, 2024
Cutaneous leishmaniasis is a parasitic disease that poses significant diagnostic challenges due to the variability of results and reliance on operator expertise. This study addresses the development of a system based on machine learning algorithms to...
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