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

Search research articles

Related Experiment Video

Updated: Jun 10, 2026

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

LAM-CATNet: lambda-aware multi-scale cross-attention swin transformer network for mammogram classification.

Gautam Ankoji¹, N Thirupathi Rao², C H V V Ramana³

¹Department of Computer Science and Engineering, Vignan's Institute of Information Technology (A), Visakhapatnam, Andhra Pradesh, 530049, India.

Scientific Reports

|June 8, 2026

Summary

Related Concept Videos

You might also read

Related Articles

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

Sort by

Same author

Ad hoc bandwidth requests and power conservation in 5G wireless networks with tiny cells.

Scientific reports·2025

Same author

Breast cancer detection using optimized hyperbolic graph attention with bidirectional convolutional neural network.

Discover oncology·2025

Same author

MediNet: ensemble transfer learning approach for classification of medical drugs-related text reviews using significant combined-embeddings.

BioData mining·2025

Same author

Investigations of Materials and Technologies Behind Environmental Pollution Monitoring Sensors for Global Health.

ACS omega·2025

Same author

Enhancing multi-class neurodegenerative disease classification using deep learning and explainable local interpretable model-agnostic explanations.

Frontiers in medicine·2025

Same author

Near-Field Microwave Resonating Sensor for Chronic Bone Assessment through Novel Electromagnetic Band Gap Structures.

ACS omega·2025

Same journal

Turbulent flow in a vortex separator with a directed pipe inlet.

Scientific reports·2026

Same journal

Systematic characteristic evaluation of clay-based cementitious material derived from calcium carbide residue and waste tile powder.

Scientific reports·2026

Same journal

Retraction Note: Improvement of a rapid diagnostic application of monoclonal antibodies against avian influenza H7 subtype virus using Europium nanoparticles.

Scientific reports·2026

Same journal

Applying large language models to spam detection in the Kazakh low-resource language setting.

Scientific reports·2026

Same journal

An open-source 3D printing system enabling in-situ freeze-thaw processing of hydrogels.

Scientific reports·2026

Same journal

An enhanced EfficientNet framework for automated waste classification using cosine annealing and label smoothing.

Scientific reports·2026

See all related articles

This summary is machine-generated.

A new deep learning model, LAM-CATNet, enhances automated breast lesion detection in mammograms. This advanced segmentation and classification tool improves early cancer identification, offering better diagnostic performance and interpretability for clinical use.

Area of Science:

Medical Imaging
Artificial Intelligence
Biomedical Engineering

Background:

Accurate segmentation and classification of breast lesions in mammograms are crucial for early breast cancer detection.
Small or difficult-to-identify lesions pose significant challenges in mammogram analysis.

Purpose of the Study:

To introduce LAM-CATNet (Lambda-Aware Multi-scale Cross-Attention Swin Transformer Network), a novel deep learning model for automated breast lesion detection.
To improve the accuracy and consistency of breast lesion segmentation and classification in mammograms.

Main Methods:

Developed LAM-CATNet, a transformer fusion model integrating statistical intensity modeling with deep learning.
Employed a Lambda Distribution-Guided Transformer-Fused Hybrid Segmentation Network approach.

Keywords:

Attention visualization Breast cancer classification Breast lesion segmentation Computer-aided diagnosis Hybrid deep learning Lambda distribution Mammographic image analysis Medical image segmentation Swin Transformer U-Net Vision Transformer

More Related Videos

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Related Experiment Videos

Last Updated: Jun 10, 2026

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

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Utilized statistical analysis of variability to enhance model performance.

Main Results:

LAM-CATNet achieved superior segmentation performance with Dice coefficients of 0.786 (CBIS-DDSM) and 0.869 (MIAS).
The model attained 94.7% accuracy and 98.2% area under the ROC curve for lesion classification.
Attention map visualizations demonstrated increased model interpretability by focusing on relevant lesion areas.

Conclusions:

LAM-CATNet effectively improves automated breast lesion segmentation and classification, outperforming existing models.
The model's high performance, interpretability, and statistical reasoning show potential as a supportive tool for computer-aided diagnosis in mammography.
Further clinical validation is recommended for its application in breast cancer screening.