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23 papers · ranked by Valyu relevance

01
AarXiv31 Aug 2017
Revisiting multiple instance neural networks
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…
97relevance
02
PPubMed3 Feb 2015
A Novel Multiple Instance Learning Method Based on Extreme Learning Machine
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…
91relevance
03
AarXiv9 Sep 2025
torchmil: A PyTorch-based library for deep Multiple Instance Learning
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…
91relevance
04
AarXiv5 Aug 2014
Multiple instance learning with bag dissimilarities
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…
91relevance
05
AarXiv2015
Characterizing Multiple Instance Datasets
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…
90relevance
06
AarXiv26 Sep 2013
Multiple Instance Learning by Discriminative Training of Markov Networks
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…
89relevance
07
AarXiv2019
Multiple instance learning with graph neural networks
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…
89relevance
08
AarXiv2015
Label Stability in Multiple Instance Learning
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…
89relevance
09
BbioRxiv1 Jan 2026
Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy
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…
89relevance
10
BbioRxiv1 Jan 2025
Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy
Christian Hallgrimson, Y. Lydia Li, Ben Cardoen, John Lim + 4 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 novel…
89relevance
11
BbioRxiv13 Apr 2020
DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning
Michael Widrich, Bernhard Schäfl, Milena Pavlović, Geir Kjetil Sandve + 3 more
High-throughput immunosequencing allows re-constructing the immune repertoire of an individual, which is an exceptional opportunity for new immunotherapies, immunodiagnostics, and vaccine design. Such immune repertoires are shaped by past and current immune events, for example infection and disease, and thus record an…
88relevance
12
PPubMed30 Apr 2026
Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy
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…
87relevance
13
BbioRxiv8 Apr 2023
Prediction of virus-host associations using protein language models and multiple instance learning
Dan Liu, Francesca Young, David L Robertson, Ke Yuan
Predicting virus-host associations is essential to determine the specific host species that viruses interact with, and discover if new viruses infect humans and animals. Currently, the host of the majority of viruses is unknown, particularly in microbiomes. To address this challenge, we introduce EvoMIL, a deep…
87relevance
14
AarXiv2021
Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder
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…
87relevance
15
PPubMed9 Oct 2013
Drug activity prediction using multiple-instance learning via joint instance and feature selection
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…
87relevance
16
PPubMed6 Feb 2017
Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction
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…
87relevance
17
BbioRxiv29 Jul 2024
Weakly supervised learning uncovers phenotypic signatures in single-cell data
Anastasia Litinetskaya, Soroor Hediyeh-zadeh, Amir Ali Moinfar, Mohammad Lotfollahi + 1 more
To deliver clinically relevant insights from large patient cohorts profiled with single-cell technologies, a key challenge is to relate sample-level and single-cell measurements. We present MultiMIL, a deep learning framework that applies attention-based multiple-instance learning for phenotype prediction and cell…
82relevance
18
PPubMed9 Jul 2024
Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science
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.…
74relevance
19
PPubMed11 Sep 2014
Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System
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…
67relevance
20
CchemRxiv21 Oct 2025
Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
65relevance
21
CchemRxiv25 Jan 2024
Deep learning for low-data drug discovery: hurdles and opportunities
Derek van Tilborg, Helena Brinkmann, Emanuele Criscuolo, Luke Rossen + 2 more
Deep learning is becoming increasingly relevant in drug discovery, from de novo design to protein structure prediction and synthesis planning. However, it is often challenged by the small data regimes typical of certain drug discovery tasks. In such scenarios, deep learning approaches – which are notoriously…
63relevance
22
CchemRxiv18 May 2022
Multi-fidelity machine learning models for improved high-throughput screening predictions
David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò
We investigate the potential of graph neural networks for transfer learning and improving molecular property prediction on sparse and expensive to acquire high-fidelity data by leveraging low-fidelity measurements as an inexpensive proxy for a targeted property ofinterest. This problem arises in discovery processes…
61relevance
23
CchemRxiv9 Aug 2021
Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
55relevance

Results for multiple instance learning

Revisiting multiple instance neural networks

A Novel Multiple Instance Learning Method Based on Extreme Learning Machine

torchmil: A PyTorch-based library for deep Multiple Instance Learning

Multiple instance learning with bag dissimilarities

Characterizing Multiple Instance Datasets

Multiple Instance Learning by Discriminative Training of Markov Networks

Multiple instance learning with graph neural networks

Label Stability in Multiple Instance Learning

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Prediction of virus-host associations using protein language models and multiple instance learning

Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder

Drug activity prediction using multiple-instance learning via joint instance and feature selection

Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science

Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Deep learning for low-data drug discovery: hurdles and opportunities

Multi-fidelity machine learning models for improved high-throughput screening predictions

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning

Revisiting multiple instance neural networks

A Novel Multiple Instance Learning Method Based on Extreme Learning Machine

torchmil: A PyTorch-based library for deep Multiple Instance Learning

Multiple instance learning with bag dissimilarities

Characterizing Multiple Instance Datasets

Multiple Instance Learning by Discriminative Training of Markov Networks

Multiple instance learning with graph neural networks

Label Stability in Multiple Instance Learning

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

Adversarial erasing enhanced multiple instance learning (siMILe): Discriminative identification of oligomeric protein structures in single molecule localization microscopy

DeepRC: Immune repertoire classification with attention-based deep massive multiple instance learning

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Prediction of virus-host associations using protein language models and multiple instance learning

Non-I.I.D. Multi-Instance Learning for Predicting Instance and Bag Labels using Variational Auto-Encoder

Drug activity prediction using multiple-instance learning via joint instance and feature selection

Multi-Instance Metric Transfer Learning for Genome-Wide Protein Function Prediction

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Federated Multi-Label Learning (FMLL): Innovative Method for Classification Tasks in Animal Science

Performance Evaluation of the Machine Learning Algorithms Used in Inference Mechanism of a Medical Decision Support System

Artificial Intelligence and Olfaction: A Survey on the Sense of Smell for Robotics

Deep learning for low-data drug discovery: hurdles and opportunities

Multi-fidelity machine learning models for improved high-throughput screening predictions

Self-supervised Clustering of Mass Spectrometry Imaging Data Using Contrastive Learning