Name
Title | ||
|---|---|---|
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction. |
Abstract | ||
|---|---|---|
Routine incorporation of this convenient approach for quantitative visualization and error reduction in histopathology aims to accelerate early adoption of CNNs into generalized real-world applications where unanticipated and previously untrained classes are often encountered. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2184-4 | BMC Bioinformatics |
Keywords | Field | DocType |
Artificial intelligence,Cancer,Convolutional neural networks,Deep learning,Diagnostics,Digital pathology,Glioblastoma,Machine learning,Neuropathology,t-SNE | Anomaly detection,Feature vector,Dimensionality reduction,Softmax function,Biology,Categorical variable,Convolutional neural network,Probability distribution,Artificial intelligence,Bioinformatics,Deep learning,Machine learning | Journal |
Volume | Issue | ISSN |
19 | 1 | 1471-2105 |
Citations | PageRank | References |
3 | 0.37 | 7 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kevin Faust | 1 | 4 | 1.07 |
| Quin Xie | 2 | 3 | 0.37 |
| Dominick Han | 3 | 3 | 0.37 |
| Kartikay Goyle | 4 | 3 | 0.37 |
| Zoya I. Volynskaya | 5 | 3 | 0.37 |
| Ugljesa Djuric | 6 | 3 | 0.37 |
| Phedias Diamandis | 7 | 7 | 0.80 |