Name
Abstract | ||
|---|---|---|
Radiomics has proven to be a powerful prognostic tool for cancer detection, and has previously been applied in lung, breast, prostate, and head-and-neck cancer studies with great success. However, these radiomics-driven methods rely on pre-defined, hand-crafted radiomic feature sets that can limit their ability to characterize unique cancer traits. In this study, we introduce a novel discovery radiomics framework where we directly discover custom radiomic features from the wealth of available medical imaging data. In particular, we leverage novel StochasticNet radiomic sequencers for extracting custom radiomic features tailored for characterizing unique cancer tissue phenotype. Using StochasticNet radiomic sequencers discovered using a wealth of lung CT data, we perform binary classification on 42,340 lung lesions obtained from the CT scans of 93 patients in the LIDC-IDRI dataset. Preliminary results show significant improvement over previous state-of-the-art methods, indicating the potential of the proposed discovery radiomics framework for improving cancer screening and diagnosis. |
Year | Venue | Field |
|---|---|---|
2015 | CoRR | Binary classification,Computer science,Medical imaging,Cancer detection,Artificial intelligence,Cancer screening,Machine learning,Cancer,Radiomics |
DocType | Volume | Citations |
Journal | abs/1511.03361 | 3 |
PageRank | References | Authors |
0.39 | 4 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. J. Shafiee | 1 | 100 | 22.85 |
| Audrey G. Chung | 2 | 26 | 8.91 |
| devinder kumar | 3 | 10 | 1.41 |
| Farzad Khalvati | 4 | 81 | 12.74 |
| Masoom Haider | 5 | 172 | 22.45 |
| Alexander Wong | 6 | 196 | 12.52 |