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Ultrasonography

Ultrasonography

Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
During an ultrasonography procedure, a handheld device called...

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Weakly Supervised Deep Learning in Radiology.

Leo Misera¹, Gustav Müller-Franzes¹, Daniel Truhn¹

¹From the Institute and Polyclinic for Diagnostic and Interventional Radiology (L.M.), Else Kröner Fresenius Center for Digital Health (L.M., J.N.K.), and Department of Medicine I (J.N.K.), Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Fetscherstrasse 74, 01307 Dresden, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Aachen, Aachen, Germany (G.M.F., D.T.); and Medical Oncology, National Center for Tumor Diseases (NCT), University Hospital Heidelberg, Heidelberg, Germany (J.N.K.).

|July 23, 2024

Summary

This summary is machine-generated.

Weakly supervised learning offers a scalable approach to training deep learning (DL) models in radiology by utilizing imperfect labels. This method unlocks large datasets for advancing AI in medical imaging analysis.

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Deep learning (DL) is the standard for AI in radiological image analysis.
Traditional DL models require extensive manual expert labeling, limiting scalability.
Weakly supervised learning (WSL) offers a more scalable alternative.

Purpose of the Study:

To outline key concepts of WSL in radiology.
To provide an overview of WSL applications in radiologic image analysis.
To highlight the potential of WSL in facilitating DL adoption and biomarker development.

Main Methods:

Exploration of WSL principles, including incomplete, inexact, and inaccurate supervision.
Discussion of using large language models for automated weak label extraction from radiology reports.
Review of current and potential applications of WSL in radiologic analysis.

Main Results:

WSL can leverage large, imperfectly labeled datasets for DL model training.
Automated weak label extraction from free-text reports is a viable approach.
WSL can overcome data limitations inherent in traditional supervised methods.

Conclusions:

WSL is crucial for unlocking the potential of large datasets in radiology.
Further research can accelerate the integration of WSL into clinical and research workflows.
WSL can drive the development of new DL-based biomarkers and enhance AI adoption in radiology.