ProMIL: Probabilistic Multiple Instance Learning for Medical Imaging

Autor:	Struski, Łukasz, Rymarczyk, Dawid, Lewicki, Arkadiusz, Sabiniewicz, Robert, Tabor, Jacek, Zieliński, Bartosz
Rok vydání:	2023
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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. Comment: Accepted Paper to European Conference on Artificial Intelligence (ECAI 2023)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2306.10535 Zobrazit plný text záznamu View this record from Arxiv