Multiple instance learning: A survey of problem characteristics and applications
TLDR
A comprehensive survey of the characteristics which define and differentiate the types of MIL problems is provided, providing insight on how the problem characteristics affect MIL algorithms, recommendations for future benchmarking and promising avenues for research.About:
This article is published in Pattern Recognition.The article was published on 2017-01-01 and is currently open access. It has received 486 citations till now. The article focuses on the topics: Instance-based learning & Supervised learning.read more
Citations
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Journal ArticleDOI
Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis
TL;DR: In this article, a survey of semi-supervised, multiple instance and transfer learning in medical image segmentation is presented, and connections between these learning scenarios, and opportunities for future research are discussed.
Journal ArticleDOI
Multiple instance learning for histopathological breast cancer image classification
P. J. Sudharshan,Caroline Petitjean,Fabio Alexandre Spanhol,Luiz S. Oliveira,Laurent Heutte,Paul Honeine +5 more
TL;DR: The comparison between MIL and single instance classification reveals the relevance of the MIL paradigm for the task at hand, and allows to obtain comparable or better results than conventional (single instance) classification without the need to label all the images.
Posted Content
Attention-based Deep Multiple Instance Learning
TL;DR: In this paper, a neural network-based permutation-invariant aggregation operator is proposed to learn the Bernoulli distribution of the bag label, where the bag-label probability is fully parameterized by neural networks.
Journal ArticleDOI
Accurate Screening of COVID-19 Using Attention-Based Deep 3D Multiple Instance Learning
TL;DR: This paper proposes an attention-based deep 3D multiple instance learning (AD3D-MIL) where a patient-level label is assigned to a 3D chest CT that is viewed as a bag of instances, which can semantically generate deep3D instances following the possible infection area.
Proceedings ArticleDOI
Graph Convolutional Label Noise Cleaner: Train a Plug-And-Play Action Classifier for Anomaly Detection
TL;DR: A graph convolutional network is devised that propagates supervisory signals from high-confidence snippets to low-confidence ones and is capable of providing cleaned supervision for action classifiers.
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