Name
Abstract | ||
|---|---|---|
Our deep neural network enables classification of out-of-focus microscope images with both higher accuracy and greater precision than previous approaches via interpretable patch-level focus and certainty predictions. The use of synthetically defocused images precludes the need for a manually annotated training dataset. The model also generalizes to different image and cell types. The framework for model training and image prediction is available as a free software library and the pre-trained model is available for immediate use in Fiji (ImageJ) and CellProfiler. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1186/s12859-018-2087-4 | BMC Bioinformatics |
Keywords | Field | DocType |
CellProfiler,Deep learning,Defocus,Focus,Image analysis,Image quality,ImageJ,Machine learning,Open-source | Biology,Autofocus,Pattern recognition,Medical imaging,Image quality,Software,Pixel,Artificial intelligence,Deep learning,Bioinformatics,Artificial neural network,Hoechst stain | Journal |
Volume | Issue | ISSN |
19 | 1 | 1471-2105 |
Citations | PageRank | References |
3 | 0.44 | 4 |
Authors | ||
13 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Samuel J. Yang | 1 | 17 | 1.53 |
| Marc Berndl | 2 | 49 | 2.38 |
| D. Michael Ando | 3 | 3 | 0.78 |
| Mariya Barch | 4 | 3 | 0.44 |
| Arunachalam Narayanaswamy | 5 | 144 | 7.66 |
| Eric M. Christiansen | 6 | 64 | 4.61 |
| Stephan Hoyer | 7 | 35 | 2.04 |
| Chris Roat | 8 | 3 | 0.44 |
| Jane Hung | 9 | 3 | 0.44 |
| Curtis Rueden | 10 | 83 | 10.55 |
| Asim Shankar | 11 | 4 | 0.81 |
| Steven Finkbeiner | 12 | 3 | 0.44 |
| Philip Nelson | 13 | 3 | 0.44 |