Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography

What can Artificial Intelligence infer from computed tomography images of smokers? In this Journal publication, Sierra’s researchers in union with Brigham and Women’s Hospital and Partners develop staging and prognosis models for subjects suffering from COPD. The neural networks are thoroughly tested in several large databases, obtaining high areas under the curve. The full article…

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Biomedical Image Processing with Containers and Deep Learning

Data architecture, artificial intelligence, automated processing, containerization, and clusters orchestration ease the transition from data acquisition to insights in medium‐to‐large datasets. In this journal paper, Sierra’s researchers, in collaboration with PNP Research Corporation (MA, USA) and the Wellman Center for Photomedicine (Boston, MA, USA), discuss how to handle large microscopy datasets and process them automatically…

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H-EM

H-EM: an algorithm for simultaneous cell diameter and intensity quantification in low-resolution imaging cytometry Sierra’s Researcher in collaboration with URJC and Madrid-MIT M+Vision Consortium have published a PlosOne paper on low-resolution cell analysis. You can find the whole article at: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0222265

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Plasmonic Nanoparticle-based Digital Cytometry to Quantify MUC16 Binding on the Surface of Leukocytes in Ovarian Cancer

Sierra Research’s team has developed the algorithms that lead to the data of the following article: https://pubs.acs.org/doi/abs/10.1021/acssensors.0c00567 Plasmonic nanoparticles conjugated with anti-CA125 antibody were used to quantify MUC16 binding on leukocytes of ovarian cancer patients. Results show an elevated presence of MUC16 on ovarian cnacer patients with respect to healthy controls. Further, the assay was…

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