AIMC Topic: Microscopy

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A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment.

Scientific data Oct 27, 2020
We present a new large-scale three-fold annotated microscopy image dataset, aiming to advance the plant cell biology research by exploring different cell microstructures including cell size and shape, cell wall thickness, intercellular space, etc. in...
Deep Learning Image Processing, Computer-Assisted Microscopy Plant Tubers Solanum tuberosum Plant Cells Neural Networks, Computer
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Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy.

Proceedings of the National Academy of Sciences of the United States of America Oct 23, 2020
Taxonomic resolution is a major challenge in palynology, largely limiting the ecological and evolutionary interpretations possible with deep-time fossil pollen data. We present an approach for fossil pollen analysis that uses optical superresolution ...
Microscopy South America Phylogeography Africa, Western Machine Learning Phylogeny Africa Pollen Fossils Neural Networks, Computer
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Pseudo-nuclear staining of cells by deep learning improves the accuracy of automated cell counting in a label-free cellular population.

Journal of bioscience and bioengineering Oct 17, 2020
Deep learning has emerged as a breakthrough tool for the segmentation of images without supporting human experts. Here, we propose an automated approach that uses deep learning to generate pseudo-nuclear staining of cells from phase contrast images. ...
Deep Learning Staining and Labeling Cell Count Microscopy Humans Automation
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Optimization of an automated tumor-infiltrating lymphocyte algorithm for improved prognostication in primary melanoma.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc Oct 1, 2020
Tumor-infiltrating lymphocytes (TIL) have potential prognostic value in melanoma and have been considered for inclusion in the American Joint Committee on Cancer (AJCC) staging criteria. However, interobserver discordance continues to prevent the ado...
Disease Progression Diagnosis, Computer-Assisted Lymphocytes, Tumor-Infiltrating Predictive Value of Tests Melanoma Male Skin Neoplasms Progression-Free Survival Middle Aged Reproducibility of Results Automation, Laboratory Risk Assessment Microscopy Neoplasm Staging Adult Databases, Factual Risk Factors Image Interpretation, Computer-Assisted Biopsy Retrospective Studies Tumor Microenvironment Humans Lymphocyte Count Aged Female Neoplasm Recurrence, Local Machine Learning
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Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.

Virchows Archiv : an international journal of pathology Sep 26, 2020
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...
Translocation, Genetic Deep Learning Humans Genetic Predisposition to Disease Phenotype Reproducibility of Results Immunohistochemistry Staining and Labeling Microscopy Biomarkers, Tumor Proof of Concept Study Predictive Value of Tests In Situ Hybridization, Fluorescence Lymphoma, Large B-Cell, Diffuse Image Interpretation, Computer-Assisted Antigens, CD20 Proto-Oncogene Proteins c-myc
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Performance of a deep learning based neural network in the selection of human blastocysts for implantation.

eLife Sep 15, 2020
Deep learning in in vitro fertilization is currently being evaluated in the development of assistive tools for the determination of transfer order and implantation potential using time-lapse data collected through expensive imaging hardware. Assistiv...
Deep Learning Image Processing, Computer-Assisted Fertilization in Vitro Microscopy Pregnancy Outcome Pregnancy Blastocyst Female Adult Male Humans Algorithms
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NuSeT: A deep learning tool for reliably separating and analyzing crowded cells.

PLoS computational biology Sep 14, 2020
Segmenting cell nuclei within microscopy images is a ubiquitous task in biological research and clinical applications. Unfortunately, segmenting low-contrast overlapping objects that may be tightly packed is a major bottleneck in standard deep learni...
Humans Artifacts Image Processing, Computer-Assisted Computational Biology Software Microscopy Cell Nucleus Algorithms HeLa Cells Deep Learning
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Interactive machine learning for fast and robust cell profiling.

PloS one Sep 11, 2020
Automated profiling of cell morphology is a powerful tool for inferring cell function. However, this technique retains a high barrier to entry. In particular, configuring image processing parameters for optimal cell profiling is susceptible to cognit...
Image Processing, Computer-Assisted Humans Machine Learning Bayes Theorem Algorithms Microscopy
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Mimicry Embedding Facilitates Advanced Neural Network Training for Image-Based Pathogen Detection.

mSphere Sep 9, 2020
The use of deep neural networks (DNNs) for analysis of complex biomedical images shows great promise but is hampered by a lack of large verified data sets for rapid network evolution. Here, we present a novel strategy, termed "mimicry embedding," for...
Host-Pathogen Interactions Vaccinia virus Toxoplasma Neural Networks, Computer Zebrafish Deep Learning Artificial Intelligence Microscopy Image Processing, Computer-Assisted Animals
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Deep Learning for Assessing the Corneal Endothelium from Specular Microscopy Images up to 1 Year after Ultrathin-DSAEK Surgery.

Translational vision science & technology Aug 21, 2020
PURPOSE: To present a fully automatic method to estimate the corneal endothelium parameters from specular microscopy images and to use it to study a one-year follow-up after ultrathin Descemet stripping automated endothelial keratoplasty.
Reproducibility of Results Deep Learning Cell Count Microscopy Endothelium, Corneal
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