Provable Multi-instance Deep AUC Maximization with Stochastic Pooling

• URL: http://arxiv.org/abs/2305.08040v4
• Date: Tue, 6 Jun 2023 05:32:49 GMT
• Title: Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
• Authors: Dixian Zhu, Bokun Wang, Zhi Chen, Yaxing Wang, Milan Sonka, Xiaodong Wu, Tianbao Yang
• Abstract summary: This paper considers a novel application of deep AUC (DAM) for multi-instance learning (MIL) A single class label is assigned to a bag of instances (e.g., multiple 2D slices of a scan for a patient)
• Score: 39.46116380220933
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper considers a novel application of deep AUC maximization (DAM) for multi-instance learning (MIL), in which a single class label is assigned to a bag of instances (e.g., multiple 2D slices of a CT scan for a patient). We address a neglected yet non-negligible computational challenge of MIL in the context of DAM, i.e., bag size is too large to be loaded into {GPU} memory for backpropagation, which is required by the standard pooling methods of MIL. To tackle this challenge, we propose variance-reduced stochastic pooling methods in the spirit of stochastic optimization by formulating the loss function over the pooled prediction as a multi-level compositional function. By synthesizing techniques from stochastic compositional optimization and non-convex min-max optimization, we propose a unified and provable muli-instance DAM (MIDAM) algorithm with stochastic smoothed-max pooling or stochastic attention-based pooling, which only samples a few instances for each bag to compute a stochastic gradient estimator and to update the model parameter. We establish a similar convergence rate of the proposed MIDAM algorithm as the state-of-the-art DAM algorithms. Our extensive experiments on conventional MIL datasets and medical datasets demonstrate the superiority of our MIDAM algorithm.

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