ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

Łukasz Struski; Dawid Rymarczyk; Arkadiusz Lewicki; Robert Sabiniewicz; Jacek Tabor; Bartosz Zieliński

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arXiv · 2306.10535

ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

Łukasz Struski·Dawid Rymarczyk·Arkadiusz Lewicki·Robert Sabiniewicz·Jacek Tabor·Bartosz Zieliński

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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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eess.IV cs.CV cs.LG

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Łukasz Struski, Dawid Rymarczyk, Arkadiusz Lewicki, Robert Sabiniewicz, Jacek Tabor, Bartosz Zieliński. 2023-06-18. ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging. https://arxiv.org/abs/2306.10535

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