14 papers · ranked by Valyu relevance
Xinggang Wang, Yongluan Yan, Peng Tang, Xiang Bai + 1 more
—Recently neural networks and multiple instance learning are both attractive topics in Artificial Intelligence related research fields. Deep neural networks have achieved great success in supervised learning problems, and multiple instance learning as a typical weakly-supervised learning method is effective for many…
Jie Wang, Liangjian Cai, Jinzhu Peng, Yuheng Jia
Since real-world data sets usually contain large instances, it is meaningful to develop efficient and effective multiple instance learning (MIL) algorithm. As a learning paradigm, MIL is different from traditional supervised learning that handles the classification of bags comprising unlabeled instances. In this paper…
Francisco M. Castro-Macías, Francisco Javier Sáez-Maldonado, Pablo Morales-Álvarez, Rafael Molina
Multiple Instance Learning (MIL) is a powerful framework for weakly supervised learning, particularly useful when fine-grained annotations are unavailable. Despite growing interest in deep MIL methods, the field lacks standardized tools for model development, evaluation, and comparison, which hinders reproducibility…
Veronika Cheplygina, David M. J. Tax, Marco Loog
Multiple instance learning (MIL) is concerned with learning from sets (bags) of objects (instances), where the individual instance labels are ambiguous. In this setting, supervised learning cannot be applied directly. Often, specialized MIL methods learn by making additional assumptions about the relationship of the…
Veronika Cheplygina, David M. J. Tax
> Abstract. In many pattern recognition problems, a single feature vector is not sufficient to describe an object. In multiple instance learning (MIL), objects are represented by sets (bags) of feature vectors (instances). This requires an adaptation of standard supervised classifiers in order to train and evaluate on…
Hossein Hajimirsadeghi, Jinling Li, Greg Mori, Mohammad Zaki + 1 more
'Tarek Sayed'] We introduce a graphical framework for multiple instance learning (MIL) based on Markov networks. This framework can be used to model the traditional MIL definition as well as more general MIL definitions. Different levels of ambiguity – the portion of positive instances in a bag – can be explored in…
Ming Tu, Jing Huang, Xiaodong He, Bowen Zhou
Multiple instance learning (MIL) aims to learn the mapping between a bag of instances and the bag-level label. In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural…
Veronika Cheplygina, Lauge Sørensen, David M. J. Tax, Marleen de Bruijne + 1 more
'Marleen de Bruijne' 'Marco Loog'] Abstract. We address the problem of instance label stability in multiple instance learning (MIL) classifiers. These classifiers are trained only on globally annotated images (bags), but often can provide fine-grained annotations for image pixels or patches (instances). This is…
Christian Hallgrimson, Y. Lydia Li, Claire A. Shou, Ben Cardoen + 5 more
Single-molecule localization microscopy (SMLM) achieves nanoscale imaging of complex protein structures in the cell. However, the ability to capture structural variability across cell conditions (cell lines, gene expression, treatment) from 3D point cloud SMLM data remains limited. We present siMILe, a weakly…
Weijia Zhang
Multi-instance learning is a type of weakly supervised learning. It deals with tasks where the data is a set of bags and each bag is a set of instances. Only the bag labels are observed whereas the labels for the instances are unknown. An important advantage of multi-instance learning is that by representing objects as…
Zhendong Zhao, Gang Fu, Sheng Liu, Khaled M Elokely + 3 more
'Robert J Doerksen' 'Yixin Chen' 'Dawn E Wilkins'] Background In drug discovery and development, it is crucial to determine which conformers (instances) of a given molecule are responsible for its observed biological activity and at the same time to recognize the most representative subset of features (molecular…
Yonghui Xu, Huaqing Min, Qingyao Wu, Hengjie Song + 1 more
Multi-Instance (MI) learning has been proven to be effective for the genome-wide protein function prediction problems where each training example is associated with multiple instances. Many studies in this literature attempted to find an appropriate Multi-Instance Learning (MIL) method for genome-wide protein function…
Bita Ghasemkhani, Ozlem Varliklar, Yunus Dogan, Semih Utku + 6 more
Simple Summary This study addresses the classification task in animal science, which helps organize and analyze complex data, essential for making informed decisions. It introduces Federated Multi-Label Learning (FMLL), a novel approach combining federated learning principles with a multi-label learning technique.…
Mert Bal, M. Fatih Amasyali, Hayri Sever, Guven Kose + 1 more
The importance of the decision support systems is increasingly supporting the decision making process in cases of uncertainty and the lack of information and they are widely used in various fields like engineering, finance, medicine, and so forth, Medical decision support systems help the healthcare personnel to select…