Name
Abstract | ||
|---|---|---|
Recent years have witnessed the rise of pathomics as a mean to describe histopathological images with quantitative biomarkers for predictive and prognostic ends, combining digital pathology, omic science and artificial intelligence. This novel research branch is the counterpart of radiomics which pursues the same aims extracting knowledge from radiological images. In this paper, we present the des... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CBMS52027.2021.00092 | 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS) |
Keywords | DocType | ISBN |
Training,Roads,Transfer learning,Pipelines,Lung cancer,Computer architecture,Prognostics and health management | Conference | 978-1-6654-4121-6 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
10 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Charles Z. Liu | 1 | 0 | 0.34 |
| Rosa Sicilia | 2 | 0 | 2.37 |
| Matteo Tortora | 3 | 0 | 0.34 |
| Ermanno Cordelli | 4 | 0 | 0.34 |
| Lorenzo Nibid | 5 | 0 | 0.34 |
| Giovanna Sabarese | 6 | 0 | 0.34 |
| Giuseppe Perrone | 7 | 0 | 0.34 |
| Michele Fiore | 8 | 0 | 0.34 |
| Sara Ramella | 9 | 0 | 0.34 |
| Paolo Soda | 10 | 0 | 0.34 |