Multiple instance classification: Review, taxonomy and comparative study
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TLDR
This work concludes that methods that extract global bag-level information show a clearly superior performance in general and permits us to establish guidelines in the design of new MIL methods.About:
This article is published in Artificial Intelligence.The article was published on 2013-08-01 and is currently open access. It has received 561 citations till now.read more
Citations
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A brief introduction to weakly supervised learning
TL;DR: This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, Where the training data are given with only coarse-grained labels; and inaccurate supervision,Where the given labels are not always ground-truth.
Proceedings ArticleDOI
Patch-Based Convolutional Neural Network for Whole Slide Tissue Image Classification
TL;DR: A novel Expectation-Maximization (EM) based method is formulated that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches and applies it to the classification of glioma and non-small-cell lung carcinoma cases into subtypes.
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: A survey of problem characteristics and applications
TL;DR: 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.
Journal ArticleDOI
Revisiting multiple instance neural networks
TL;DR: This article revisit Multiple Instance Neural Networks (MINNs) that the neural networks aim at solving the MIL problems and proposes a new type of MINN to learn bag representations, which is different from the existing MINNs that focus on estimating instance label.
References
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Robust Real-Time Face Detection
Paul A. Viola,Michael Jones +1 more
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Proceedings ArticleDOI
Robust real-time face detection
Paul A. Viola,Michael Jones +1 more
TL;DR: A new image representation called the “Integral Image” is introduced which allows the features used by the detector to be computed very quickly and a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions.
Journal Article
Statistical Comparisons of Classifiers over Multiple Data Sets
TL;DR: A set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers is recommended: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparisons of more classifiers over multiple data sets.
A Practical Guide to Support Vector Classication
TL;DR: A simple procedure is proposed, which usually gives reasonable results and is suitable for beginners who are not familiar with SVM.