Name
Title | ||
|---|---|---|
Partial dependence of breast tumor malignancy on ultrasound image features derived from boosted trees |
Abstract | ||
|---|---|---|
Various computerized features extracted from breast ultrasound images are useful in assessing the malignancy of breast tumors. However, the underlying relationship between the computerized features and tumor malignancy may not be linear in nature. We use the decision tree ensemble trained by the cost-sensitive boosting algorithm to approximate the target function for malignancy assessment and to reflect this relationship qualitatively. Partial dependence plots are employed to explore and visualize the effect of features on the output of the decision tree ensemble. In the experiments, 31 image features are extracted to quantify the sonographic characteristics of breast tumors. Patient age is used as an external feature because of its high clinical importance. The area under the receiver-operating characteristic curve of the tree ensembles can reach 0.95 with sensitivity of 0.95 (61/64) at the associated specificity 0.74 (77/104). The partial dependence plots of the four most important features are demonstrated to show the influence of the features on malignancy, and they are in accord with the empirical observations. The results can provide visual and qualitative references on the computerized image features for physicians, and can be useful for enhancing the interpretability of computer-aided diagnosis systems for breast ultrasound. (C) 2010 SPIE and IS&T. [DOI: 10.1117/1.3385763] |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1117/1.3385763 | JOURNAL OF ELECTRONIC IMAGING |
Keywords | Field | DocType |
cancer,ultrasonography,feature extraction,decision tree,ultrasound,image features,receiver operating characteristic curve,pattern recognition | Breast ultrasound,Decision tree,Interpretability,Computer vision,Pattern recognition,Computer science,Feature (computer vision),Feature extraction,Malignancy,Boosting (machine learning),Artificial intelligence,Ultrasound image | Journal |
Volume | Issue | ISSN |
19 | 2 | 1017-9909 |
Citations | PageRank | References |
0 | 0.34 | 15 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wei Yang | 1 | 10 | 1.74 |
| Su Zhang | 2 | 60 | 9.39 |
| Wenying Li | 3 | 11 | 2.01 |
| Yaqing Chen | 4 | 11 | 2.35 |
| Hongtao Lu | 5 | 735 | 93.14 |
| Wufan Chen | 6 | 511 | 59.06 |
| Yazhu Chen | 7 | 96 | 13.10 |