AIMC Topic: Microscopy
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Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
European journal of cancer (Oxford, England : 1990)
Sep 8, 2021
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...
Mobile microscopy and telemedicine platform assisted by deep learning for the quantification of Trichuris trichiura infection.
PLoS neglected tropical diseases
Sep 7, 2021
Soil-transmitted helminths (STH) are the most prevalent pathogens among the group of neglected tropical diseases (NTDs). The Kato-Katz technique is the diagnosis method recommended by the World Health Organization (WHO) although it often presents a d...
Helminth egg analysis platform (HEAP): An opened platform for microscopic helminth egg identification and quantification based on the integration of deep learning architectures.
Journal of microbiology, immunology, and infection = Wei mian yu gan ran za zhi
Sep 2, 2021
BACKGROUND: Millions of people throughout the world suffer from parasite infections. Traditionally, technicians use manual eye inspection of microscopic specimens to perform a parasite examination. However, manual operations have limitations that hin...
Automated detection of superficial fungal infections from microscopic images through a regional convolutional neural network.
PloS one
Aug 17, 2021
Direct microscopic examination with potassium hydroxide is generally used as a screening method for diagnosing superficial fungal infections. Although this type of examination is faster than other diagnostic methods, it can still be time-consuming to...
Robust deep learning optical autofocus system applied to automated multiwell plate single molecule localization microscopy.
Journal of microscopy
Aug 13, 2021
We presenta robust, long-range optical autofocus system for microscopy utilizing machine learning. This can be useful for experiments with long image data acquisition times that may be impacted by defocusing resulting from drift of components, for ex...
Vesseg: An Open-Source Tool for Deep Learning-Based Atherosclerotic Plaque Quantification in Histopathology Images-Brief Report.
Arteriosclerosis, thrombosis, and vascular biology
Aug 12, 2021
Objective: Manual plaque segmentation in microscopy images is a time-consuming process in atherosclerosis research and potentially subject to unacceptable user-to-user variability and observer bias. We address this by releasing Vesseg a tool that inc...
Vascular Remodeling
Mice, Knockout, ApoE
Plaque, Atherosclerotic
Arteries
Atherosclerosis
Microscopy
Image Interpretation, Computer-Assisted
Diagnosis, Computer-Assisted
Deep Learning
Mice
Staining and Labeling
Disease Models, Animal
Mice, Inbred C57BL
Predictive Value of Tests
Reproducibility of Results
Animals
Male
Software
Severity of Illness Index
Female
Robust Phase Unwrapping via Deep Image Prior for Quantitative Phase Imaging.
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Aug 10, 2021
Quantitative phase imaging (QPI) is an emerging label-free technique that produces images containing morphological and dynamical information without contrast agents. Unfortunately, the phase is wrapped in most imaging system. Phase unwrapping is the ...
Automatic cell counting from stimulated Raman imaging using deep learning.
PloS one
Jul 21, 2021
In this paper, we propose an automatic cell counting framework for stimulated Raman scattering (SRS) images, which can assist tumor tissue characteristic analysis, cancer diagnosis, and surgery planning processes. SRS microscopy has promoted tumor di...
A Machine Learning Tool Using Digital Microscopy (Morphogo) for the Identification of Abnormal Lymphocytes in the Bone Marrow.
Acta cytologica
Jul 20, 2021
Morphological analysis of the bone marrow is an essential step in the diagnosis of hematological disease. The conventional analysis of bone marrow smears is performed under a manual microscope, which is labor-intensive and subject to interobserver va...
Quick Annotator: an open-source digital pathology based rapid image annotation tool.
The journal of pathology. Clinical research
Jul 19, 2021
Image-based biomarker discovery typically requires accurate segmentation of histologic structures (e.g. cell nuclei, tubules, and epithelial regions) in digital pathology whole slide images (WSIs). Unfortunately, annotating each structure of interest...
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