Najnowsze trendy i zmiany paradygmatów w branży patologii cyfrowej - Vetkompleksowo – serwis dla lekarzy weterynarii

Najnowsze trendy i zmiany paradygmatów w branży patologii cyfrowej

Piśmiennictwo
  1. Kannan S. et al.: Segmentation of glomeruli within trichrome images using deep learning. „Kidney International Reports”, 2019, 4, 7, 955-962.
  2. Martin D.R. et al.: A Deep Learning Convolutional Neural Network Can Recognize Common Patterns of Injury in Gastric Pathology. „Archives of Pathology & Laboratory Medicine”, 2019.
  3. Guo X. et al.: Liver Steatosis Segmentation With Deep Learning Methods. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019). IEEE, 2019.
  4. Yu Y. et al.: Deep learning enables automated scoring of liver fibrosis stages. „Scientific Reports”, 2018, 8, 1, 1-10.
  5. Kapil A. et al.: Deep semi supervised generative learning for automated tumor proportion scoring on NSCLC tissue needle biopsies. „Scientific Reports” 2018, 8, 1, 1-10.
  6. Aprupe L. et al. Robust and accurate quantification of biomarkers of immune cells in lung cancer micro-environment using deep convolutional neural networks. „PeerJ”, 2019, 7, e6335.
  7. Heinemann F. et al.: Deep neural network based histological scoring of lung fibrosis and inflammation in the mouse model system. „PloS one”, 2018, 13, 8.
  8. Xie Y. et al.: Deep Learning for Muscle Pathology Image Analysis. Deep Learning and Convolutional Neural Networks for Medical Imaging and Clinical Informatics. Springer, Cham, 2019, 23-41.
  9. Sing T. et al.: A deep learning-based model of normal histology. „bioRxiv”, 2019, 838417.

dr n. wet. Aleksandra Żuraw
Charles River Laboratories
22022 Trans-Canada Hwy,
Senneville, QC H9X 1C1, Kanada

Znajdź swoją kategorię

2814 praktycznych artykułów - 324 ekspertów - 22 kategorii tematycznych

Weterynaria w Terenie

Poznaj nasze serwisy