AIMC Topic: Microscopy
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Can AI-assisted microscope facilitate breast HER2 interpretation? A multi-institutional ring study.
Virchows Archiv : an international journal of pathology
Jul 19, 2021
The level of human epidermal growth factor receptor-2 (HER2) protein and gene expression in breast cancer is an essential factor in judging the prognosis of breast cancer patients. Several investigations have shown high intraobserver and interobserve...
Humans
Artificial Intelligence
Receptor, ErbB-2
Carcinoma, Ductal, Breast
Observer Variation
Automation, Laboratory
Reproducibility of Results
In Situ Hybridization, Fluorescence
China
Immunohistochemistry
Predictive Value of Tests
Microscopy
Image Interpretation, Computer-Assisted
Breast Neoplasms
Biomarkers, Tumor
Retrospective Studies
Female
Time-frequency time-space long short-term memory networks for image classification of histopathological tissue.
Scientific reports
Jul 1, 2021
Image analysis in histopathology provides insights into the microscopic examination of tissue for disease diagnosis, prognosis, and biomarker discovery. Particularly for cancer research, precise classification of histopathological images is the ultim...
Optical mesoscopy, machine learning, and computational microscopy enable high information content diagnostic imaging of blood films.
The Journal of pathology
Jun 29, 2021
Automated image-based assessment of blood films has tremendous potential to support clinical haematology within overstretched healthcare systems. To achieve this, efficient and reliable digital capture of the rich diagnostic information contained wit...
Automated Detection and Diameter Estimation for Mouse Mesenteric Artery Using Semantic Segmentation.
Journal of vascular research
Jun 28, 2021
BACKGROUND: Pressurized myography is useful for the assessment of small artery structures and function. However, this procedure requires technical expertise for sample preparation and effort to choose an appropriate sized artery. In this study, we de...
Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.
SLAS discovery : advancing life sciences R & D
Jun 24, 2021
Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While...
Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.
Histopathology
Jun 24, 2021
AIMS: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article w...
celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition.
PloS one
Jun 24, 2021
Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell dea...
Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks.
BMC bioinformatics
Jun 15, 2021
BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells an...
Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.
The American journal of pathology
Jun 12, 2021
With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from...
Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.
Nature biomedical engineering
Jun 10, 2021
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can...
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