AIMC Topic: Cell Count

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Identifying tumor cells at the single-cell level using machine learning.

Genome biology May 30, 2022
Tumors are complex tissues of cancerous cells surrounded by a heterogeneous cellular microenvironment with which they interact. Single-cell sequencing enables molecular characterization of single cells within the tumor. However, cell annotation-the a...
Machine Learning Cell Count
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High-Throughput Recognition of Tumor Cells Using Label-Free Elemental Characteristics Based on Interpretable Deep Learning.

Analytical chemistry Feb 7, 2022
With cancer seriously hampering the increasing life expectancy of people, developing an instant diagnostic method has become an urgent objective. In this work, we developed a label-free laser-induced breakdown spectroscopy (LIBS) method for high-thro...
Humans Cell Count Spectrum Analysis Neural Networks, Computer Algorithms Deep Learning
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Object detection for automatic cancer cell counting in zebrafish xenografts.

PloS one Nov 29, 2021
Cell counting is a frequent task in medical research studies. However, it is often performed manually; thus, it is time-consuming and prone to human error. Even so, cell counting automation can be challenging to achieve, especially when dealing with ...
Zebrafish Deep Learning Neoplasms, Experimental Humans Image Processing, Computer-Assisted Heterografts Neoplasm Transplantation Animals Cell Count Neoplasms
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Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet.

Scientific reports Nov 25, 2021
Counting cells in fluorescent microscopy is a tedious, time-consuming task that researchers have to accomplish to assess the effects of different experimental conditions on biological structures of interest. Although such objects are generally easy t...
Reproducibility of Results Cell Count Animals Microscopy, Fluorescence Automation, Laboratory Neurons Brain Deep Learning Image Processing, Computer-Assisted Mice
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Few-Shot Breast Cancer Metastases Classification via Unsupervised Cell Ranking.

IEEE/ACM transactions on computational biology and bioinformatics Oct 7, 2021
Tumor metastases detection is of great importance for the treatment of breast cancer patients. Various CNN (convolutional neural network) based methods get excellent performance in object detection/segmentation. However, the detection of metastases i...
Humans Neural Networks, Computer Image Interpretation, Computer-Assisted Neoplasm Metastasis Histocytochemistry Lymph Nodes Unsupervised Machine Learning Cell Count Algorithms Female Breast Neoplasms
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Multi-model CNN fusion for sperm morphology analysis.

Computers in biology and medicine Aug 24, 2021
Infertility is a common disorder affecting 20% of couples worldwide. Furthermore, 40% of all cases are related to male infertility. The first step in the determination of male infertility is semen analysis. The morphology, concentration, and motility...
Humans Neural Networks, Computer Semen Analysis Spermatozoa Algorithms Male Cell Count
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Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness.

PLoS computational biology Jul 23, 2021
Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that ...
Cell Shape Adaptation, Physiological Extracellular Matrix Biophysical Phenomena Cell Adhesion Models, Biological Vimentin Neoplasm Metastasis Computational Biology Triple Negative Breast Neoplasms Cell Proliferation Epithelial-Mesenchymal Transition Cadherins Antigens, CD Cell Count Tumor Microenvironment Biomarkers, Tumor Cell Line, Tumor Humans Machine Learning Female
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Comparison of machine learning methods to predict udder health status based on somatic cell counts in dairy cows.

Scientific reports Jul 1, 2021
Bovine mastitis is one of the most important economic and health issues in dairy farms. Data collection during routine recording procedures and access to large datasets have shed the light on the possibility to use trained machine learning algorithms...
Dairying Cattle Cell Count Mastitis, Bovine Machine Learning Animals Diagnosis, Computer-Assisted Prognosis Female
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Using Unsupervised Machine Learning to Identify Age- and Sex-Independent Severity Subgroups Among Patients with COVID-19: Observational Longitudinal Study.

Journal of medical Internet research May 27, 2021
BACKGROUND: Early detection and intervention are the key factors for improving outcomes in patients with COVID-19.
Cell Count COVID-19 Lymphocytes Humans Prognosis Datasets as Topic Monocytes Neutrophils Aspartate Aminotransferases L-Lactate Dehydrogenase Spain Triage Alanine Transaminase Longitudinal Studies C-Reactive Protein Cluster Analysis Emergency Service, Hospital Unsupervised Machine Learning
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Review of computer vision application in in vitro fertilization: the application of deep learning-based computer vision technology in the world of IVF.

Journal of assisted reproduction and genetics Apr 3, 2021
In vitro fertilization has been regarded as a forefront solution in treating infertility for over four decades, yet its effectiveness has remained relatively low. This could be attributed to the lack of advancements for the method of observing and se...
Fertilization in Vitro Treatment Outcome Cell Count Neural Networks, Computer Female Humans Pregnancy Deep Learning Image Processing, Computer-Assisted Blastocyst Time-Lapse Imaging
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