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Results for multi-instance learning

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13 papers · ranked by Valyu relevance

01
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…
87relevance
02
BbioRxiv20 Dec 2016
Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification
Wentao Zhu, Qi Lou, Yeeleng Scott Vang, Xiaohui Xie
Mammogram classification is directly related to computer-aided diagnosis of breast cancer. Traditional methods requires great effort to annotate the training data by costly manual labeling and specialized computational models to detect these annotations during test. Inspired by the success of using deep convolutional…
86relevance
03
BbioRxiv11 Jan 2025
scMILD: Single-cell Multiple Instance Learning for Sample Classification and Associated Subpopulation Discovery
Kyeonghun Jeong, Jinwook Choi, Kwangsoo Kim
Single-cell transcriptomics enables the study of cellular heterogeneity, but current unsupervised strategies make it challenging to associate individual cells with sample conditions. We propose scMILD, a weakly supervised learning framework based on Multiple Instance Learning, which leverages sample-level labels to…
83relevance
04
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…
83relevance
05
BbioRxiv12 Jun 2025
Application and Characterization of the Multiple Instance Learning Framework in Flow Cytometry
Zhiyuan Ding, Alexander Baras
For decades, flow cytometry has allowed for single-cell profiling based on selected biomarkers and is widely used in both clinical and research settings. One major limitation of most conventional flow cytometry analyses is the dependency on a mostly manual gating process. This generally involves sequentially selecting…
80relevance
06
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…
80relevance
07
BbioRxiv30 May 2021
Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects
Talia Konkle, George A. Alvarez
Anterior regions of the ventral visual stream have substantial information about object categories, prompting theories that category-level forces are critical for shaping visual representation. The strong correspondence between category-supervised deep neural networks and ventral stream representation supports this…
71relevance
08
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…
63relevance
09
BbioRxiv3 Sep 2025
Parallel Belief States account for Learning and Updating of Attentional Priorities in Multidimensional Environments
Paul Tiesinga, Thilo Womelsdorf
Inferring the behavioral relevance of visual features is difficult in multidimensional environments as many features could be important. One solution could involve tracking the experience with multiple features and using attentional control to decide which subset of features to explore and chose. Here, we characterize…
62relevance
10
BbioRxiv3 Feb 2023
Paired single-cell multi-omics data integration with Mowgli
Geert-Jan Huizing, Ina Maria Deutschmann, Gabriel Peyré, Laura Cantini
The profiling of multiple molecular layers from the same set of cells has recently become possible. There is thus a growing need for multi-view learning methods able to jointly analyze these data. We here present Multi-Omics Wasserstein inteGrative anaLysIs (Mowgli), a novel method for the integration of paired…
62relevance
11
BbioRxiv4 Nov 2023
A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models
Sen Yang, Shidan Wang, Yiqing Wang, Ruichen Rong + 5 more
Recent technological advances have highlighted the significant impact of the human microbiome and metabolites on physiological conditions. Integrating microbiome and metabolite data has shown promise in predictive capabilities. We developed a new supervised contrastive learning framework, MB-SupCon-cont, that (1)…
61relevance
12
BbioRxiv14 Jan 2025
COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer
Jerome J. Choi, Noah Cohen Kalafut, Tim Gruenloh, Corinne D. Engelman + 2 more
Single-omics approaches often provide a limited view of complex biological systems, whereas multiomics integration offers a more comprehensive understanding by combining diverse data views. However, integrating heterogeneous data types and interpreting the intricate relationships between biological features—both within…
61relevance
13
BbioRxiv23 Jan 2024
Multimodal CustOmics: A Unified and Interpretable Multi-Task Deep Learning Framework for Multimodal Integrative Data Analysis in Oncology
Hakim Benkirane, Maria Vakalopoulou, David Planchard, Julien Adam + 3 more
Characterizing cancer poses a delicate challenge as it involves deciphering complex biological interactions within the tumor’s microenvironment. Histology images and molecular profiling of tumors are often available in clinical trials and can be leveraged to understand these interactions. However, despite recent…
61relevance

Results for multi-instance learning

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

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

scMILD: Single-cell Multiple Instance Learning for Sample Classification and Associated Subpopulation Discovery

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Application and Characterization of the Multiple Instance Learning Framework in Flow Cytometry

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

Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects

MOTL: enhancing multi-omics matrix factorization with transfer learning

Parallel Belief States account for Learning and Updating of Attentional Priorities in Multidimensional Environments

Paired single-cell multi-omics data integration with Mowgli

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer

Multimodal CustOmics: A Unified and Interpretable Multi-Task Deep Learning Framework for Multimodal Integrative Data Analysis in Oncology

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

Deep Multi-instance Networks with Sparse Label Assignment for Whole Mammogram Classification

scMILD: Single-cell Multiple Instance Learning for Sample Classification and Associated Subpopulation Discovery

Weakly supervised learning uncovers phenotypic signatures in single-cell data

Application and Characterization of the Multiple Instance Learning Framework in Flow Cytometry

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

Beyond category-supervision: instance-level contrastive learning models predict human visual system responses to objects

MOTL: enhancing multi-omics matrix factorization with transfer learning

Parallel Belief States account for Learning and Updating of Attentional Priorities in Multidimensional Environments

Paired single-cell multi-omics data integration with Mowgli

A Generalized Supervised Contrastive Learning Framework for Integrative Multi-omics Prediction Models

COSIME: Cooperative multi-view integration and Scalable and Interpretable Model Explainer

Multimodal CustOmics: A Unified and Interpretable Multi-Task Deep Learning Framework for Multimodal Integrative Data Analysis in Oncology