Multiple Instance Learning with Genetic Pooling for medical data analysis

•First approach in designing Multiple Instance Pooling function through metaheuristics i.e. Genetic algorithm.•A trainable pooling method is proposed - Genetic Pooling.•Genetic Pooling outperforms other MIL pooling methods in terms of AUC and average precision score. Multiple Instance Learning is a...

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Vydáno v:Pattern recognition letters Ročník 133; s. 247 - 255
Hlavní autoři: Bhattacharjee, Kamanasish, Pant, Millie, Zhang, Yu-Dong, Satapathy, Suresh Chandra
Médium: Journal Article
Jazyk:angličtina
Vydáno: Amsterdam Elsevier B.V 01.05.2020
Elsevier Science Ltd
Témata:
Algorithms
Artificial neural networks
Data analysis
Genetic Algorithm (GA)
Genetic algorithms
Genetic analysis
Labels
Learning algorithms
Machine learning
Multiple Instance Learning (MIL)
Neural network
Neural networks
Pooling
Multiple Instance Learning (MIL)
Genetic Algorithm (GA)
Neural network
Pooling
ISSN:0167-8655, 1872-7344
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Shrnutí:•First approach in designing Multiple Instance Pooling function through metaheuristics i.e. Genetic algorithm.•A trainable pooling method is proposed - Genetic Pooling.•Genetic Pooling outperforms other MIL pooling methods in terms of AUC and average precision score. Multiple Instance Learning is a weakly supervised learning technique which is particularly well suited for medical data analysis as the class labels are often not available at desired granularity. Multiple Instance Learning through Deep Neural Networks is relatively a new paradigm in machine learning. The most important part of Multiple Instance Learning through Deep Neural Networks is designing a trainable pooling function which determines the instance-to bag relationship. In this paper, we propose a Multiple Instance pooling technique based on Genetic Algorithm called Genetic Pooling. In this technique, instance labels inside a bag are optimized by minimizing bag-level losses. The main contribution of the paper is that the bag level pooling layer for generating attention weights for bag instances are replaced by random initialization of attention weights and finding the optimized attention weights through Genetic Algorithm.
Bibliografie:ObjectType-Article-1
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ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2020.02.025