Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

Modeling in Therapy

Modeling in Therapy

Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Widespread controls of precipitation sensitivity on drought recovery of global forests.

Nature communications·2026

Same author

Robot-Assisted Lobectomy for Lung Cancer Complicated by Lymphangioleiomyomatosis.

Surgical case reports·2026

Same author

Microstructural and Biomechanical Determinants of Biological Aging.

bioRxiv : the preprint server for biology·2026

Same author

CXCL8 is associated with aflatoxin B1-triggered injury and caspase-3 activation in porcine kidney epithelial PK15 cells: integrated transcriptomics and CRISPR/Cas9 knockout.

Food and chemical toxicology : an international journal published for the British Industrial Biological Research Association·2026

Same author

Biological Aging of the Cardiopulmonary System.

bioRxiv : the preprint server for biology·2026

Same author

Predicting Autopsy-Confirmed Neuropathology across Clinical, Neuroimaging, and CSF Biomarkers using Machine Learning.

bioRxiv : the preprint server for biology·2026

Same journal

MUST: Multi-style virtual staining with incomplete pairs.

IEEE transactions on medical imaging·2026

Same journal

BrainCL: Transformer-Based Brain Network Contrastive Learning with Multi-Order Topology and Salience Masking.

IEEE transactions on medical imaging·2026

Same journal

LLM-enhanced Neuron Segmentation and Reconstruction in Complex Mouse Brain Images.

IEEE transactions on medical imaging·2026

Same journal

Matrixed-Spectrum Decomposition Accelerated Linear Boltzmann Transport Equation Solver for Fast Scatter Correction in Multi-Spectral CT.

IEEE transactions on medical imaging·2026

Same journal

The Ritz Adjoint Method for MRI Pulse Design.

IEEE transactions on medical imaging·2026

Same journal

Physiology-guided Self-supervised Learning for Simultaneous Dual-Tracer PET Separation.

IEEE transactions on medical imaging·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 11, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

MIRROR: Multi-Modal Pathological Self-Supervised Representation Learning via Modality Alignment and Retention.

Tianyi Wang, Jianan Fan, Dingxin Zhang

IEEE Transactions on Medical Imaging

|November 13, 2025

Summary

This summary is machine-generated.

This study introduces MIRROR, a novel framework for integrating histopathology and transcriptomics data in cancer diagnostics. MIRROR effectively aligns and retains unique features from both modalities, improving cancer subtyping and survival analysis.

More Related Videos

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Related Experiment Videos

Last Updated: Jan 11, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Multimodal Protocol for Assessing Metacognition and Self-Regulation in Adults with Learning Difficulties

Published on: September 27, 2020

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

Area of Science:

Oncology
Computational Biology
Bioinformatics

Background:

Histopathology and transcriptomics are key cancer diagnostic modalities, offering morphological and molecular insights.
Current multi-modal learning often overlooks modality-specific structures, especially with heterogeneous data like histopathology and transcriptomics.
Histopathology provides spatial and cellular context, while transcriptomics offers gene expression patterns, presenting integration challenges due to their disparate nature.

Purpose of the Study:

To develop a novel multi-modal representation learning framework, MIRROR, that balances modality alignment and retention for cancer diagnostics.
To address the challenge of integrating heterogeneous histopathology and transcriptomics data while preserving modality-specific information.
To enhance cancer subtyping and survival analysis through improved oncological feature representation.

Main Methods:

Utilized dedicated encoders for comprehensive feature extraction from histopathology and transcriptomics data.
Implemented a modality alignment module for integrating phenotype patterns and molecular profiles.
Incorporated a modality retention module to preserve unique attributes and a style clustering module to refine disease-relevant information.

Main Results:

MIRROR demonstrated superior performance in cancer subtyping and survival analysis on TCGA cohorts.
The framework effectively constructed comprehensive oncological feature representations by integrating diverse data modalities.
Evaluations confirmed the model's ability to balance alignment and retention of modality-specific features.

Conclusions:

MIRROR offers an effective approach for multi-modal representation learning in oncology.
The framework's ability to integrate heterogeneous data enhances cancer diagnosis and prognostic predictions.
MIRROR's code availability facilitates further research and application in cancer diagnostics.