A convex method for classification of groups of examples

TL;DR

This paper introduces a convex optimization-based method for group classification, improving performance in applications like medical image analysis by directly optimizing group-level objectives.

Contribution

It proposes a novel convex approach that synthesizes weak supervision and candidate labeling, optimizing for group performance in classification tasks.

Findings

Effective on large-scale image classification for polyp detection.

Significant performance improvements with modified SVM penalty functions.

Handles hundreds of millions of examples efficiently.

Abstract

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.