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Deep Learning and Its Applications in Computational Pathology
| Content Provider | MDPI |
|---|---|
| Author | David, Fenyö Hong, Runyu |
| Copyright Year | 2022 |
| Description | Deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and graph neural networks (GNNs) have, over the past decade, changed the accuracy of prediction in many diverse fields. In recent years, the application of deep learning techniques in computer vision tasks in pathology has demonstrated extraordinary potential in assisting clinicians, automating diagnoses, and reducing costs for patients. Formerly unknown pathological evidence, such as morphological features related to specific biomarkers, copy number variations, and other molecular features, could also be captured by deep learning models. In this paper, we review popular deep learning methods and some recent publications about their applications in pathology. |
| Ending Page | 168 |
| Page Count | 10 |
| Starting Page | 159 |
| e-ISSN | 26737426 |
| DOI | 10.3390/biomedinformatics2010010 |
| Journal | BioMedInformatics |
| Issue Number | 1 |
| Volume Number | 2 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-02-03 |
| Access Restriction | Open |
| Subject Keyword | BioMedInformatics Biomedinformatics Deep Learning Machine Learning Histopathology Computational Pathology Convolutional Neural Networks Generative Adversarial Networks |
| Content Type | Text |
| Resource Type | Article |
