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

Improving Translational Accuracy

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

You might also read

Related Articles

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

Sort by

Same author

Artificial neural network modeling to predict corrective stress of a two-layer composite plate under fully reversed cyclic loading using the finite element method & morrow method.

Scientific reports·2026

Same author

AI-enabled eye-movement and emerging multimodal frameworks for precision dyslexia screening and reading pattern analysis.

Frontiers in medicine·2026

Same author

Advancing breast cancer diagnosis through ultrasound imaging: A cross-attention multi-scale vision transformer approach with comparative analysis against ResNet architectures.

Medicine·2026

Same author

Metaheuristic-driven machine learning study for early detection and classification of Parkinson's disease using feature prioritization with pelican optimization algorithm.

Medicine·2026

Same author

Smartphone-Assisted Chemiluminescence System to Detect Fe in Drinking Water Samples.

Luminescence : the journal of biological and chemical luminescence·2026

Same author

Comparative unfolding of the Trp-cage miniprotein in anionic and cationic surfactants.

PloS one·2026

Search research articles

Related Experiment Video

Updated: Sep 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Explainable multi-view transformer framework with mutual learning for precision breast cancer pathology image

Haewon Byeon¹, Mahmood Alsaadi², Richa Vijay³

¹Convergence Department, Korea University of Technology and Education, Cheonan, Republic of Korea.

Frontiers in Oncology

|July 29, 2025

Summary

A new AI framework, Multi-View Transformer Online Fusion Mutual Learning (MVT-OFML), enhances breast cancer diagnosis by combining CNNs and Transformers. This interpretable model improves accuracy and provides visual explanations for clinical decisions.

Keywords:

MVT-OFML breast cancer explainable AI multi-view transformer mutual learning pathology image classification

More Related Videos

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Related Experiment Videos

Last Updated: Sep 13, 2025

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images

Published on: April 21, 2023

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

Area of Science:

Artificial Intelligence
Medical Image Analysis
Computational Pathology

Background:

Accurate breast cancer diagnosis relies on pathology image analysis, but current AI models struggle to balance performance and explainability.
Convolutional Neural Networks (CNNs) excel at local details but miss global context, while Transformers capture global context but lack fine-grained local feature modeling.

Purpose of the Study:

To develop a novel, interpretable AI framework for breast cancer pathology image classification that overcomes limitations of existing models.
To advance Explainable AI (XAI) in precision cancer diagnosis through a hybrid approach.

Main Methods:

Proposed MVT-OFML (Multi-View Transformer Online Fusion Mutual Learning) framework integrating ResNet-50 for local features and a multi-view Transformer for global context.
Implemented Online Fusion Mutual Learning (OFML) for bidirectional knowledge sharing between CNN and Transformer components.
Generated interpretable attention maps and feature visualizations for model transparency.

Main Results:

MVT-OFML significantly outperformed baseline models on BreakHis and BACH datasets.
Achieved accuracy improvements of 0.90% (BreakHis) and 2.26% (BACH).
Demonstrated F1-score gains of 4.75% (BreakHis) and 3.21% (BACH).

Conclusions:

MVT-OFML offers a promising AI solution for precise and interpretable breast cancer diagnosis and prognosis.
The framework enhances clinical usability by providing transparent decision-making processes.
Integrating complementary AI paradigms with explainable strategies supports informed clinical decision-making.