Evaluating Multi-class Multiple-Instance Learning for Image Categorization

2007 ◽  
pp. 155-165 ◽  
Author(s):  
Xinyu Xu ◽  
Baoxin Li
Keyword(s):  
Multiple Instance Learning ◽  
Image Categorization

MULTIPLE CLASS MULTIPLE-INSTANCE LEARNING AND ITS APPLICATION TO IMAGE CATEGORIZATION

2007 ◽  
Vol 07 (03) ◽  
pp. 427-444 ◽  
Author(s):  
XINYU XU ◽  
BAOXIN LI
Keyword(s):  
Feature Space ◽  
Training Sample ◽  
Training Image ◽  
Multiple Instance Learning ◽  
Support Vector ◽  
Final Decision ◽  
Learning Approach ◽  
Image Categorization ◽  
Binary Classifiers ◽  
Multiple Class

We propose a Multiple Class Multiple-Instance (MCMI) learning approach and demonstrate its application to the problem of image categorization. Our method extends the binary Multiple-Instance learning approach for image categorization. Instead of constructing a set of binary classifiers (each trained to separate one category from the rest) and then making the final decision based on the winner of all the binary classifiers, our method directly allows the computation of a multi-class classifier by first projecting each training image onto a multi-class feature space and then simultaneously minimizing the multi-class objective function in a Support Vector Machine framework. The multi-class feature space is constructed based on the instance prototypes obtained by Multiple-Instance learning which treats an image as a set of instances with training labels being associated with images rather than instances. The experiment results on two challenging data sets demonstrate that our method achieved better classification accuracy and is less sensitive to the training sample size compared with traditional one-versus-the-rest binary MI classification methods.

Predicting Adverse Events and their Precursors in Aviation Using Multi-Class Multiple-Instance Learning

2021 ◽  
Author(s):  
Marc-Henri Bleu-Laine ◽  
Tejas G. Puranik ◽  
Dimitri N. Mavris ◽  
Bryan Matthews
Keyword(s):  
Adverse Events ◽  
Multiple Instance Learning

Space Target Image Categorization Based on the Second Representation

2014 ◽  
Vol 35 (5) ◽  
pp. 1247-1251
Author(s):  
Fei-yun Jiang ◽  
Rui Sun ◽  
Xu-dong Zhang ◽  
Chao Li
Keyword(s):  
Image Categorization ◽  
Target Image ◽  
Space Target

MILL: Channel Attention–based Deep Multiple Instance Learning for Landslide Recognition

2021 ◽  
Vol 17 (2s) ◽  
pp. 1-11
Author(s):  
Xiaochuan Tang ◽  
Mingzhe Liu ◽  
Hao Zhong ◽  
Yuanzhen Ju ◽  
Weile Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Large Scale ◽  
Remote Sensing Image ◽  
Disaster Risk ◽  
Multiple Instance Learning ◽  
Remote Sensing Images ◽  
Loess Area ◽  
Remote Sensing Image Classification ◽  
Natural Disaster Risk

Landslide recognition is widely used in natural disaster risk management. Traditional landslide recognition is mainly conducted by geologists, which is accurate but inefficient. This article introduces multiple instance learning (MIL) to perform automatic landslide recognition. An end-to-end deep convolutional neural network is proposed, referred to as Multiple Instance Learning–based Landslide classification (MILL). First, MILL uses a large-scale remote sensing image classification dataset to build pre-train networks for landslide feature extraction. Second, MILL extracts instances and assign instance labels without pixel-level annotations. Third, MILL uses a new channel attention–based MIL pooling function to map instance-level labels to bag-level label. We apply MIL to detect landslides in a loess area. Experimental results demonstrate that MILL is effective in identifying landslides in remote sensing images.

scholarly journals Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marleen M. Nieboer ◽  
Luan Nguyen ◽  
Jeroen de Ridder
Keyword(s):  
Multiple Instance Learning ◽  
Cancer Diagnostics ◽  
Common Mechanism ◽  
Open Chromatin ◽  
Driver Genes ◽  
3D Genome ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Cancer Types ◽  
The Impact

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

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