Bayesian multiple instance learning: automatic feature selection and inductive transfer
Proceedings of the 25th international conference on Machine learning
-SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
IEEE Transactions on Signal Processing
Greed is good: algorithmic results for sparse approximation
IEEE Transactions on Information Theory
Efficient minimization for dictionary based sparse representation and signal recovery
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Find the intrinsic space for multiclass classification
Proceedings of the 4th International Symposium on Applied Sciences in Biomedical and Communication Technologies
Context enhanced graphical model for object localization in medical images
MCV'12 Proceedings of the Second international conference on Medical Computer Vision: recognition techniques and applications in medical imaging
Computer Methods and Programs in Biomedicine
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Classification is one of the core problems in computer-aided cancer diagnosis (CAD) via medical image interpretation. High detection sensitivity with reasonably low false positive (FP) rate is essential for any CAD system to be accepted as a valuable or even indispensable tool in radiologists' workflow. In this paper, we propose a novel classification framework based on sparse representation. It first builds an overcomplete dictionary of atoms for each class via K-SVD learning, then classification is formulated as sparse coding which can be solved efficiently. This representation naturally generalizes for both binary and multiwise classification problems, and can be used as a standalone classifier or integrated with an existing decision system. Our method is extensively validated in CAD systems for both colorectal polyp and lung nodule detection, using hospital scale, multi-site clinical datasets. The results show that we achieve superior classification performance than existing state-of-the-arts, using support vector machine (SVM) and its variants [1, 2], boosting [3], logistic regression [4], relevance vector machine (RVM) [5,6], or k-nearest neighbor (KNN) [7].