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  1. Home
  3. Medlp-hafb-clip: Hierarchical Adaptive Large Model With Learnable Medical Prompts For Level Ii Ultrasound Standard Plane Identification.
  1. Home
  3. Medlp-hafb-clip: Hierarchical Adaptive Large Model With Learnable Medical Prompts For Level Ii Ultrasound Standard Plane Identification.

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Related Experiment Video

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound
05:04

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Published on: August 9, 2024

MedLP-HAFB-CLIP: Hierarchical Adaptive Large Model With Learnable Medical Prompts for Level II Ultrasound Standard

Jiaxin Cai1, Chenquan Dai2, Runqing Xiong3

  • 1School of Mathematics and Statistics, Xiamen University of Technology, Xiamen, China.

Ultrasound in Medicine & Biology
|June 27, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new AI model, MedLP-HAFB-CLIP, accurately classifies fetal ultrasound images, distinguishing between lateral ventricle and thalamus sections. This tool aids in prenatal diagnosis by improving standardization and reliability in ultrasound screening.

Keywords:
Fetal ultrasound image classificationHierarchical Adaptive Feature BlockMedical Learnable Prompt Strategy

Related Experiment Videos

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound
05:04

Using Simulation Models to Train Clinicians in the Use of Point-of-Care Ultrasound

Published on: August 9, 2024

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Prenatal Diagnosis

Background:

  • Level II ultrasound standard section classification is crucial for prenatal diagnosis.
  • Sonographers struggle to differentiate lateral ventricle and thalamus transverse sections.

Purpose of the Study:

  • To develop an automated tool for classifying fetal ultrasound standard planes.
  • To enhance the accuracy and reliability of prenatal ultrasound screening.

Main Methods:

  • A dataset of 1261 second-trimester fetal ultrasound images was used.
  • A novel model, MedLP-HAFB-CLIP, incorporating a prompt learner and HAFB module was proposed.
  • The model integrates medical knowledge and enhances feature extraction for improved classification.

Main Results:

  • MedLP-HAFB-CLIP significantly outperformed baseline models in classifying lateral ventricle and thalamus transverse sections.
  • The model achieved perfect classification in a small observer study, reducing interpretation time.
  • Preliminary findings require validation in larger reader studies.

Conclusions:

  • The proposed MedLP-HAFB-CLIP is a robust tool for automating fetal ultrasound standard plane classification.
  • This method promises to improve standardization and reliability in clinical prenatal ultrasound screening.