Multiple instance learning for computer aided detection and diagnosis of gastric cancer with dual-energy CT imaging.

Multiple instance learning algorithms have been increasingly utilized in computer aided detection and diagnosis field. In this study, we propose a novel multiple instance learning method for the identification of tumor invasion depth of gastric cancer with dual-energy CT imaging. In the proposed scheme, two level features, bag-level features and instance-level features are extracted for subsequent processing and classification work. For instance-level features, there is some ambiguity in assigning labels to selected patches. An improved Citation-KNN method is presented to solve this problem. Compared with benchmarking state-of-the-art multiple instance learning algorithms using the same clinical dataset, the proposed algorithm can achieve improved results. The experimental evaluation is performed using leave-one-out cross validation with the total accuracy of 0.7692. The proposed multiple instance learning algorithm serves as an alternative method for computer aided diagnosis and identification of tumor invasion depth of gastric cancer with dual-energy CT imaging techniques.