An embarrassingly simple approach to neural multiple instance classification

TL;DR

This paper introduces a straightforward neural network approach for multiple instance learning that uses a bag-level ranking loss, eliminating the need for specialized architecture modifications, and demonstrates competitive results on standard benchmarks.

Contribution

The paper proposes a simple, architecture-agnostic bag-level ranking loss for neural MIL, simplifying implementation while maintaining or improving performance.

Findings

Effective on standard MIL benchmarks

Comparable or better than existing methods

Applicable to various neural architectures

Abstract

Multiple Instance Learning (MIL) is a weak supervision learning paradigm that allows modeling of machine learning problems in which labels are available only for groups of examples called bags. A positive bag may contain one or more positive examples but it is not known which examples in the bag are positive. All examples in a negative bag belong to the negative class. Such problems arise frequently in fields of computer vision, medical image processing and bioinformatics. Many neural network based solutions have been proposed in the literature for MIL, however, almost all of them rely on introducing specialized blocks and connectivity in the architectures. In this paper, we present a novel and effective approach to Multiple Instance Learning in neural networks. Instead of making changes to the architectures, we propose a simple bag-level ranking loss function that allows Multiple Instance Classification in any neural architecture. We have demonstrated the effectiveness of our proposed method for popular MIL benchmark datasets. In addition, we have tested the performance of our method in convolutional neural networks used to model an MIL problem derived from the well-known MNIST dataset. Results have shown that despite being simpler, our proposed scheme is comparable or better than existing methods in the literature in practical scenarios. Python code files for all the experiments can be found at https://github.com/amina01/ESMIL.