Name
Abstract | ||
|---|---|---|
In this work we propose an image-retrieval based approach for case-adaptive classifier design in computer-aided diagnosis (CAD). The traditional approach in CAD is to first train a pattern-classifier based on a set of existing training samples, and then apply this classifier to subsequent new cases. In our proposed approach, we will first apply image-retrieval to obtain a set of lesion images from a library of known cases that have similar image features to a case being diagnosed (i.e., query). These retrieved cases are then used to optimize a pattern-classifier toward boosting its classification accuracy on the query case. In our experiments the proposed retrieval-driven approach was tested on a library of mammogram images from 589 cases (331 benign, 258 malignant), and was demonstrated to yield significant improvement in classification performance. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5652421 | ICIP |
Keywords | Field | DocType |
mammography,training samples,query case,computer aided diagnosis,image retrieval,pattern classification,support vector machine (svm),case-adaptive classification,case-adaptive classifier,image classification,classification accuracy,lesion images,patient diagnosis,computer-aided diagnosis,medical image processing,mammogram images,cancer,image features,support vector machines,design automation,support vector machine | Computer science,Computer-aided diagnosis,Image retrieval,Artificial intelligence,Contextual image classification,Classifier (linguistics),CAD,Computer vision,Pattern recognition,Feature (computer vision),Support vector machine,Boosting (machine learning),Machine learning | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 2 |
PageRank | References | Authors |
0.51 | 6 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hao Jing | 1 | 9 | 3.46 |
| Yongyi Yang | 2 | 1409 | 140.74 |