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Related Experiment Video

Updated: Jun 7, 2025

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Detection of Architectural Distortion in Prior Mammograms via Analysis of Oriented Patterns

Published on: August 30, 2013

Breast Lesion Detection for Ultrasound Images Using MaskFormer.

Aashna Anand¹, Seungho Jung², Sukhan Lee³

¹High School, Korea International School, Seongnam 13543, Republic of Korea.

Sensors (Basel, Switzerland)

|November 9, 2024

Summary

This summary is machine-generated.

MaskFormer significantly improves breast lesion segmentation on ultrasound images, outperforming other models. This AI approach enhances detection accuracy, reducing errors and aiding clinicians in identifying subtle lesions.

Keywords:

MaskFormer benign breast lesion deep learning malignant ultrasound

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Breast cancer detection via ultrasound faces challenges including low contrast and operator variability.
Conventional Convolutional Neural Network (CNN) models like U-Net show performance limitations, such as early model loss plateauing.

Purpose of the Study:

To evaluate the MaskFormer model's efficacy in segmenting and classifying breast lesions using ultrasound.
To compare MaskFormer's performance against existing models for breast lesion detection.

Main Methods:

The study employed the MaskFormer deep learning model for image segmentation and classification tasks.
Comparative analysis was conducted against U-Net and other CNN-based models using ultrasound images of breast lesions.

Main Results:

MaskFormer demonstrated continuous improvement and superior performance compared to other models.
The model achieved high precision and recall rates for malignant lesions, with a mean average precision (mAP) of 0.943.
MaskFormer significantly reduced false positives and false negatives in breast lesion detection.

Conclusions:

MaskFormer offers enhanced precision and recall for breast lesion segmentation and classification in ultrasound images.
The AI model shows potential in detecting subtle lesions missed by human practitioners, improving diagnostic reliability.
Integrating MaskFormer could advance ultrasound-based breast cancer detection, offering operator-independent analysis and potentially improving patient outcomes.