Ensemble-Based Instance Relevance Estimation in Multiple-Instance Learning

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.

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

Scientific Reports ◽

10.1038/s41598-021-93917-y ◽

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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Instance elimination strategy for non-convex multiple-instance learning using sparse positive bags

Neural Networks ◽

10.1016/j.neunet.2021.07.009 ◽

2021 ◽

Author(s):

Min Yuan ◽

Yitian Xu ◽

Renxiu Feng ◽

Zongmin Liu

Keyword(s):

Multiple Instance Learning

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Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽

10.3390/medicina57060527 ◽

2021 ◽

Vol 57 (6) ◽

pp. 527

Author(s):

Vijay Vyas Vadhiraj ◽

Andrew Simpkin ◽

James O’Connell ◽

Naykky Singh Singh Ospina ◽

Spyridoula Maraka ◽

...

Keyword(s):

Decision Making ◽

Sensitivity And Specificity ◽

Thyroid Nodules ◽

Median Filter ◽

Ultrasound Image ◽

Multiple Instance Learning ◽

Image Features ◽

Classification Model ◽

Support Vector ◽

Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

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