Rethinking Multiple Instance Learning: Developing an Instance-Level Classifier via Weakly-Supervised Self-Training

TL;DR

This paper introduces a novel weakly-supervised self-training approach for multiple instance learning, effectively utilizing all instances and improving detection of hard positive instances, leading to state-of-the-art results across various datasets.

Contribution

It formulates MIL as a semi-supervised instance classification problem and proposes a weakly-supervised self-training method to better utilize labeled and unlabeled instances.

Findings

Achieved new state-of-the-art performance on multiple datasets.

Effectively detects hard positive instances near the decision boundary.

Demonstrated robustness across synthetic, benchmark, and histopathology datasets.

Abstract

Multiple instance learning (MIL) problem is currently solved from either bag-classification or instance-classification perspective, both of which ignore important information contained in some instances and result in limited performance. For example, existing methods often face difficulty in learning hard positive instances. In this paper, we formulate MIL as a semi-supervised instance classification problem, so that all the labeled and unlabeled instances can be fully utilized to train a better classifier. The difficulty in this formulation is that all the labeled instances are negative in MIL, and traditional self-training techniques used in semi-supervised learning tend to degenerate in generating pseudo labels for the unlabeled instances in this scenario. To resolve this problem, we propose a weakly-supervised self-training method, in which we utilize the positive bag labels to construct a global constraint and a local constraint on the pseudo labels to prevent them from degenerating and force the classifier to learn hard positive instances. It is worth noting that easy positive instances are instances are far from the decision boundary in the classification process, while hard positive instances are those close to the decision boundary. Through iterative optimization, the pseudo labels can gradually approach the true labels. Extensive experiments on two MNIST synthetic datasets, five traditional MIL benchmark datasets and two histopathology whole slide image datasets show that our method achieved new SOTA performance on all of them. The code will be publicly available.