AIMC Topic: Microscopy
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Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT.
Computational and mathematical methods in medicine
Jan 12, 2022
Infection diseases are among the top global issues with negative impacts on health, economy, and society as a whole. One of the most effective ways to detect these diseases is done by analysing the microscopic images of blood cells. Artificial intell...
Grade classification of human glioma using a convolutional neural network based on mid-infrared spectroscopy mapping.
Journal of biophotonics
Jan 10, 2022
This study proposes a convolutional neural network (CNN)-based computer-aided diagnosis (CAD) system for the grade classification of human glioma by using mid-infrared (MIR) spectroscopic mappings. Through data augmentation of pixels recombination, t...
A novel deep learning-based 3D cell segmentation framework for future image-based disease detection.
Scientific reports
Jan 10, 2022
Cell segmentation plays a crucial role in understanding, diagnosing, and treating diseases. Despite the recent success of deep learning-based cell segmentation methods, it remains challenging to accurately segment densely packed cells in 3D cell memb...
Quantifying the cell morphology and predicting biological behavior of signet ring cell carcinoma using deep learning.
Scientific reports
Jan 7, 2022
Signet ring cell carcinoma (SRCC) is a malignant tumor of the digestive system. This tumor has long been considered to be poorly differentiated and highly invasive because it has a higher rate of metastasis than well-differentiated adenocarcinoma. Bu...
Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos.
Fertility and sterility
Jan 5, 2022
OBJECTIVE: To perform a series of analyses characterizing an artificial intelligence (AI) model for ranking blastocyst-stage embryos. The primary objective was to evaluate the benefit of the model for predicting clinical pregnancy, whereas the second...
Unsupervised discovery of dynamic cell phenotypic states from transmitted light movies.
PLoS computational biology
Dec 30, 2021
Identification of cell phenotypic states within heterogeneous populations, along with elucidation of their switching dynamics, is a central challenge in modern biology. Conventional single-cell analysis methods typically provide only indirect, static...
Deep learning identifies inflamed fat as a risk factor for lymph node metastasis in early colorectal cancer.
The Journal of pathology
Dec 28, 2021
The spread of early-stage (T1 and T2) adenocarcinomas to locoregional lymph nodes is a key event in disease progression of colorectal cancer (CRC). The cellular mechanisms behind this event are not completely understood and existing predictive biomar...
Humans
Predictive Value of Tests
Lymphatic Metastasis
Diagnosis, Computer-Assisted
Proof of Concept Study
Retrospective Studies
Neoplasm Staging
Risk Factors
Deep Learning
Reproducibility of Results
Biopsy
Colorectal Neoplasms
Lymph Nodes
Microscopy
Risk Assessment
Image Interpretation, Computer-Assisted
Early Detection of Cancer
Adipose Tissue
Assessing red blood cell deformability from microscopy images using deep learning.
Lab on a chip
Dec 21, 2021
Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of t...
[EMPAIA-ecosystem for pathology diagnostics with AI assistance].
Der Pathologe
Dec 17, 2021
Applications of deep learning and other artificial intelligence techniques play an increasing role in pathological research. In contrast to research, applications in clinical routine are rare so far, although the first certified solutions have alread...
Mitotic Index Determination on Live Cells From Label-Free Acquired Quantitative Phase Images Using a Supervised Autoencoder.
IEEE/ACM transactions on computational biology and bioinformatics
Dec 8, 2021
This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera ...
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