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23 papers · ranked by Valyu relevance

01
AarXiv2022
Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences
Younghoon Kim, Tao Wang, Danyi Xiong, Xinlei Wang + 1 more
Early detection of cancers has been much explored due to its paramount importance in biomedical fields. Among different types of data used to answer this biological question, studies based on T cell receptors (TCRs) are under recent spotlight due to the growing appreciation of the roles of the host immunity system in…
89relevance
02
PPubMed23 Dec 2022
Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports
Kokten Ulas Birant, Derya Birant, Jaesung Lee
The aim of this study is to develop a new approach to be able to correctly predict the outcome of electronic sports (eSports) matches using machine learning methods. Previous research has emphasized player-centric prediction and has used standard (single-instance) classification techniques. However, a team-centric…
87relevance
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…
85relevance
04
AarXiv4 Jun 2026
In-Context Multiple Instance Learning
Alexander Möllers, Marvin Sextro, Julius Hense, Gabriel Dernbach + 1 more
Multiple Instance Learning (MIL) addresses problems where supervision is available at the level of bags of instances and has been successfully applied in fields ranging from computational pathology to satellite imagery. Nevertheless, existing algorithms struggle in the low-label regime that characterizes many…
84relevance
05
PPubMed24 Feb 2025
Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework
Xu Zhang, Chenlong Li, Weisi Chen, Jiaxin Zheng + 1 more
In recent years, the number of people suffering from depression has gradually increased, and early detection is of great significance for the well-being of the public. However, the current methods for detecting depression are relatively limited, typically relying on the self-rating depression scale (SDS) and…
84relevance
06
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…
83relevance
07
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…
82relevance
08
BbioRxiv3 Aug 2023
Neo-epitope identification by weakly-supervised peptide-TCR binding prediction
Yuli Gao, Yicheng Gao, Wannian Li, Siqi Wu + 7 more
The identification of T cell neo-epitopes is fundamental and computational challenging in tumor immunotherapy study. As the binding of pMHC - T cell receptor (TCR) is the essential condition for neo-epitopes to trigger the cytotoxic T cell reactivity, several computational studies have been proposed to predict…
82relevance
09
AarXiv29 Oct 2025
Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning
Harvey, Ethan, Loevlie, Dennis Johan + 2 more
Multiple instance learning (MIL) is often used in medical imaging to classify high-resolution 2D images by processing patches or classify 3D volumes by processing slices. However, conventional MIL approaches treat instances separately, ignoring contextual relationships such as the appearance of nearby patches or slices…
80relevance
10
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…
80relevance
11
BbioRxiv29 Jul 2024
Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases
Anastasia Litinetskaya, Maiia Shulman, Soroor Hediyeh-zadeh, Amir Ali Moinfar + 5 more
Multimodal analysis of single-cell samples from healthy and diseased tissues at various stages provides a comprehensive view that identifies disease-specific cells, their molecular features and aids in patient stratification. Here, we present MultiMIL, a novel weakly-supervised multimodal model designed to construct…
77relevance
12
AarXiv18 Mar 2024
Counting Network for Learning from Majority Label
Kaito Shiku, Shinnosuke Matsuo, Daiki Suehiro, Ryoma Bise
The paper proposes a novel problem in multi-class Multiple-Instance Learning (MIL) called Learning from the Majority Label (LML). In LML, the majority class of instances in a bag is assigned as the bag's label. LML aims to classify instances using bag-level majority classes. This problem is valuable in various…
76relevance
13
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.…
70relevance
14
PPubMed19 Sep 2022
A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data
Xiaoli Jiang, Jing Zhou, Xinyue Qiao, Chang Peng + 1 more
In this paper, a novel distance-based multilabel classification algorithm is proposed. The proposed algorithm combines k-nearest neighbors (kNN) with neighborhood classifier (NC) to impose double constraints on the quantity and distance of the neighbors. In short, the radius constraint is introduced in the kNN model to…
70relevance
15
AarXiv2 Dec 2021
Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention
Kun Yan, Chenbin Zhang, Jun Hou, Ping Wang + 3 more
Word Vector Guided Attention Authors: ['Kun Yan' 'Chenbin Zhang' 'Jun Hou' 'Ping Wang' 'Zied Bouraoui' 'Shoaib Jameel' 'Steven Schockaert'] Multi-label few-shot image classification (ML-FSIC) is the task of assigning descriptive labels to previously unseen images, based on a small number of training examples. A key…
64relevance
16
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…
62relevance
17
BbioRxiv25 Mar 2024
MOTL: enhancing multi-omics matrix factorization with transfer learning
David Hirst, Morgane Térézol, Laura Cantini, Paul Villoutreix + 2 more
Joint matrix factorization is a popular method for extracting lower dimensional representations of multi-omics data. It disentangles underlying mixtures of biological signals, facilitating efficient sample clustering, disease subtyping, or biomarker identification, for instance. However, when a multi-omics dataset is…
61relevance
18
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
19
PPubMed31 Jul 2023
Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning
M. Priyadharshini, A. Faritha Banu, Bhisham Sharma, Subrata Chowdhury + 3 more
'Subrata Chowdhury' 'Khaled Rabie' 'Thokozani Shongwe' 'Faheem Khan'] In recent years, both machine learning and computer vision have seen growth in the use of multi-label categorization. SMOTE is now being utilized in existing research for data balance, and SMOTE does not consider that nearby examples may be from…
61relevance
20
CchemRxiv2 Jun 2025
Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach
Authors not listed
Ensuring the trustworthiness of machine learning (ML) models in high-stake applications is crucial. One such application is predicting anti-cancer drug sensitivity, where ML models are built with the final goal of integrating them into treatment recommendation systems for personalized medicine. Here, we propose a…
60relevance
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…
58relevance
22
AarXiv18 Dec 2024
The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling
Mauro Andrés Nievas Offidani, Facundo Roffet, Claudio Delrieux, Maria Carolina Gonzalez Galtier + 1 more
classifiers through problem transformation, ontology engineering, and model ensembling Authors: ['Mauro Andrés Nievas Offidani' 'Facundo Roffet' 'Claudio Delrieux' 'Maria Carolina Gonzalez Galtier' 'Marcos Zárate'] Abstract. Classification is a fundamental task in machine learning. While conventional methods—such as…
57relevance
23
CchemRxiv23 Dec 2025
Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data
Authors not listed
We present a new method for fingerprint- ing atomic configurations relevant to ML-IAM training and application, utilizing the ChIMES descriptor. These fingerprints enable rigor- ous analysis of statistical distinguishability be- tween configurations. Sample applications in- clude assessing diversity within ML-IAP…
52relevance

Results for multi-instance learning

Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences

Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports

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

In-Context Multiple Instance Learning

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework

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

Neo-epitope identification by weakly-supervised peptide-TCR binding prediction

Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

Counting Network for Learning from Majority Label

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

A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data

Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention

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

MOTL: enhancing multi-omics matrix factorization with transfer learning

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

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning

Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach

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

The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data

Multiple Instance Neural Networks Based on Sparse Attention for Cancer Detection using T-cell Receptor Sequences

Multi-Objective Multi-Instance Learning: A New Approach to Machine Learning for eSports

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

In-Context Multiple Instance Learning

Optimizing depression detection in clinical doctor-patient interviews using a multi-instance learning framework

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

Neo-epitope identification by weakly-supervised peptide-TCR binding prediction

Synthetic Data Reveals Generalization Gaps in Correlated Multiple Instance Learning

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Multimodal weakly supervised learning to identify disease-specific changes in single-cell atlases

Counting Network for Learning from Majority Label

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

A Neighborhood Model with Both Distance and Quantity Constraints for Multilabel Data

Inferring Prototypes for Multi-Label Few-Shot Image Classification with Word Vector Guided Attention

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

MOTL: enhancing multi-omics matrix factorization with transfer learning

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

Hybrid Multi-Label Classification Model for Medical Applications Based on Adaptive Synthetic Data and Ensemble Learning

Increasing trustworthiness of machine learning-based drug sensitivity prediction with a multivariate random forest approach

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

The Multiplex Classification Framework: optimizing multi-label classifiers through problem transformation, ontology engineering, and model ensembling

Cluster-Graph Fingerprinting: A Framework for Quantitative Analysis of Machine-Learned Interatomic Model Training and Simulation Data