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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Updated: Jun 11, 2025

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

MV-Swin-T: MAMMOGRAM CLASSIFICATION WITH MULTI-VIEW SWIN TRANSFORMER.

Sushmita Sarker¹, Prithul Sarker¹, George Bebis¹

¹Department of Computer Science and Engineering, University of Nevada, Reno, USA.

Proceedings. IEEE International Symposium on Biomedical Imaging

|October 7, 2024

Summary

This summary is machine-generated.

This study introduces a novel transformer-based network for multi-view mammographic image classification, enhancing breast cancer detection by preserving inter-view correlations. The approach effectively integrates information across different views for improved diagnostic accuracy.

Keywords:

Breast Mass Classification Mammogram Multi-view Transformer

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Last Updated: Jun 11, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Traditional deep learning for breast cancer classification often uses single-view mammograms, missing crucial correlations radiologists use in practice.
Existing multi-view methods may lose vital inter-view information due to independent view processing or simple fusion.

Purpose of the Study:

To propose an innovative multi-view network based exclusively on transformers for mammographic image classification.
To effectively capture and integrate inter-view correlations in mammograms for improved tumor detection.

Main Methods:

Developed a novel multi-view network utilizing transformers.
Introduced a shifted window-based dynamic attention block for enhanced multi-view information integration.
Evaluated the model on CBIS-DDSM and Vin-Dr Mammo datasets.

Main Results:

The transformer-based approach demonstrated effective integration of multi-view information.
Coherent transfer of spatial features between mammographic views was promoted.
Comparative analysis showed the performance of transformer models in diverse settings.

Conclusions:

The proposed transformer network addresses limitations in existing multi-view mammography analysis.
This method facilitates better utilization of inter-view correlations for improved breast cancer classification.
The study provides a foundation for advanced AI in medical image analysis.