In this paper we examine data fusion methods for multi-view data classification. We present a decision concept which explicitly takes into account the input multi-view structure, where for each case there is a different subset of relevant views. The proposed method, which we dub Mixture of Views, is implemented by a special purpose neural network architecture. It is demonstrated on the task of classifying breast microcalcifications as benign or malignant based on several mammography views. The single view decisions are combined by a data-driven decision, according to the relevance of each view in a given case, into a global decision. The method is evaluated on a large multi-view dataset extracted from the standardized digital database for screening mammography (DDSM). The experimental results show that our method outperforms previously suggested fusion methods.
|Title of host publication||ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging|
|Publisher||IEEE Computer Society|
|Number of pages||5|
|State||Published - Apr 2019|
|Event||16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy|
Duration: 8 Apr 2019 → 11 Apr 2019
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||16th IEEE International Symposium on Biomedical Imaging, ISBI 2019|
|Period||8/04/19 → 11/04/19|
Bibliographical notePublisher Copyright:
© 2019 IEEE.
- Deep learning
- Lesion classification
- Mixture of experts