ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

• URL: http://arxiv.org/abs/2306.10535v2
• Date: Tue, 12 Mar 2024 11:57:00 GMT
• Title: ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging
• Authors: {\L}ukasz Struski, Dawid Rymarczyk, Arkadiusz Lewicki, Robert Sabiniewicz, Jacek Tabor, Bartosz Zieli\'nski
• Abstract summary: Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. We introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein estimation. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications.
• Score: 13.355864185650745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multiple Instance Learning (MIL) is a weakly-supervised problem in which one label is assigned to the whole bag of instances. An important class of MIL models is instance-based, where we first classify instances and then aggregate those predictions to obtain a bag label. The most common MIL model is when we consider a bag as positive if at least one of its instances has a positive label. However, this reasoning does not hold in many real-life scenarios, where the positive bag label is often a consequence of a certain percentage of positive instances. To address this issue, we introduce a dedicated instance-based method called ProMIL, based on deep neural networks and Bernstein polynomial estimation. An important advantage of ProMIL is that it can automatically detect the optimal percentage level for decision-making. We show that ProMIL outperforms standard instance-based MIL in real-world medical applications. We make the code available.

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