Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Topics Community

Article

Multi-diagnosis multi-instance learning for auxiliary gene mutation diagnosis in whole slide images.

Pathology, research and practice - 1 Oct 2026

Liu Ao, Liu Yang, Li Wentao, Liu Chen, Qin Xiaoying, Li Zhongyu, Chen Yifeng, Jin Long, Liang Baobao

Abstract excerpt

BACKGROUND AND OBJECTIVE: Multi-Instance Learning (MIL) is widely applied in the representation of high-resolution whole slide images (WSIs) in pathology. However, there are still challenges in associating WSIs with genetic representations. The connection between the types of genetic mutations in WSI and their histological images is not intuitive, making it difficult for traditional models to effectively capture...

Topics

Join the communities discussing this publication.

Image Interpretation, Computer-Assisted
Lung Neoplasms
Mutation
Neoplasms

Source

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Identifiers

PMID: 42235341
DOI: 10.1016/j.prp.2026.156576

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 31 Aug 2026.

Community interpretation

Discuss the evidence

Ask a question, add context or challenge an interpretation. Community activity is separate from scientific evidence strength.

0

discussions

0

replies

Citations encouraged

Claims remain attributable.

No discussion yet

Be the first to add a careful, cited interpretation.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Multi-diagnosis multi-instance learning for auxiliary gene mutation diagnosis in whole slide images.PMID 42235341Read ↗

Targeting tumor heterogeneity: multiplex-detection-based multiple instance learning for whole slide image classification.Semantic proximity Read ↗

Histopathological bladder cancer gene mutation prediction with hierarchical deep multiple-instance learning.Semantic proximity Read ↗

M4: Multi-proxy multi-gate mixture of experts network for multiple instance learning in histopathology image analysis.Semantic proximity Read ↗

Optimization of deep learning models for the prediction of gene mutations using unsupervised clustering.Semantic proximity Read ↗

A novel transformer-based aggregation model for predicting gene mutations in lung adenocarcinoma.Semantic proximity Read ↗

Flexible and Highly-Efficient Feature Perception for Molecular Traits Prediction via Self-interactive Deep LearningSemantic proximity Read ↗

Accurate spatial quantification in computational pathology with multiple instance learningSemantic proximity Read ↗

Prediction of <i>TP53</i> biomarkers and survival outcomes from whole slide images using a vision transformer-based multi-instance learning frameworkSemantic proximity Read ↗

Predicting tumor mutation burden and VHL mutation from renal cancer pathology slides with self-supervised deep learning.Semantic proximity Read ↗

A Deep Learning Framework Integrating Tumor Microenvironmental Features Accurately Predicts Multiple Driver Gene Mutations in Lung Cancer Pathology Images.Semantic proximity Read ↗

Ensemble transformer-based multiple instance learning to predict pathological subtypes and tumor mutational burden from histopathological whole slide images of endometrial and colorectal cancer.Semantic proximity Read ↗

Tackling Tumor Heterogeneity Issue: Transformer-Based Multiple Instance Enhancement Learning for Predicting EGFR Mutation via CT Images.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.