A convex method for classification of groups of examples
This work addresses the need for better group-level classification in applications like medical imaging, though it appears incremental as it synthesizes existing approaches.
The authors tackled the problem of group-level classification, where performance is measured per group rather than per individual example, by proposing a convex optimization method that directly optimizes the group objective. Their approach demonstrated improved results on an image classification task for polyp detection in capsule endoscopy, handling hundreds of millions of examples efficiently.
There are many applications where it important to perform well on a set of examples as opposed to individual examples. For example in image or video classification the question is does an object appear somewhere in the image or video while there are several candidates of the object per image or video. In this context, it is not important what is the performance per candidate. Instead the performance per group is the ultimate objective. For such problems one popular approach assumes weak supervision where labels exist for the entire group and then multiple instance learning is utilized. Another approach is to optimize per candidate, assuming each candidate is labeled, in the belief that this will achieve good performance per group. We will show that better results can be achieved if we offer a new methodology which synthesizes the aforementioned approaches and directly optimizes for the final optimization objective while consisting of a convex optimization problem which solves the global optimization problem. The benefit of grouping examples is demonstrated on an image classification task for detecting polyps in images from capsule endoscopy of the colon. The algorithm was designed to efficiently handle hundreds of millions of examples. Furthermore, modifications to the penalty function of the standard SVM algorithm, have proven to significantly improve performance in our test case.