AIMC Topic: Ultrasonography, Mammary

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A deep learning approach for early prediction of breast cancer neoadjuvant chemotherapy response on multistage bimodal ultrasound images.

BMC medical imaging Jan 23, 2025
Neoadjuvant chemotherapy (NAC) is a systemic and systematic chemotherapy regimen for breast cancer patients before surgery. However, NAC is not effective for everyone, and the process is excruciating. Therefore, accurate early prediction of the effic...
Neural Networks, Computer Female Breast Neoplasms Humans Ultrasonography, Mammary Image Interpretation, Computer-Assisted Ultrasonography, Doppler, Color Middle Aged Deep Learning Neoadjuvant Therapy
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ATEDU-NET: An Attention-Embedded Deep Unet for multi-disease diagnosis in chest X-ray images, breast ultrasound, and retina fundus.

Computers in biology and medicine Jan 21, 2025
In image segmentation for medical image analysis, effective upsampling is crucial for recovering spatial information lost during downsampling. This challenge becomes more pronounced when dealing with diverse medical image modalities, which can signif...
Fundus Oculi Retina Radiography, Thoracic Humans Female Deep Learning Image Processing, Computer-Assisted Ultrasonography, Mammary
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Enhancing lesion detection in automated breast ultrasound using unsupervised multi-view contrastive learning with 3D DETR.

Medical image analysis Jan 18, 2025
The inherent variability of lesions poses challenges in leveraging AI in 3D automated breast ultrasound (ABUS) for lesion detection. Traditional methods based on single scans have fallen short compared to comprehensive evaluations by experienced sono...
Image Interpretation, Computer-Assisted Algorithms Female Breast Neoplasms Imaging, Three-Dimensional Unsupervised Machine Learning Ultrasonography, Mammary Humans
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Effective BCDNet-based breast cancer classification model using hybrid deep learning with VGG16-based optimal feature extraction.

BMC medical imaging Jan 8, 2025
PROBLEM: Breast cancer is a leading cause of death among women, and early detection is crucial for improving survival rates. The manual breast cancer diagnosis utilizes more time and is subjective. Also, the previous CAD models mostly depend on manma...
Image Interpretation, Computer-Assisted Breast Neoplasms Deep Learning Ultrasonography, Mammary Humans Algorithms Female
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Explainable attention based breast tumor segmentation using a combination of UNet, ResNet, DenseNet, and EfficientNet models.

Scientific reports Jan 6, 2025
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such...
Humans Algorithms Image Processing, Computer-Assisted Ultrasonography, Mammary Image Interpretation, Computer-Assisted Female Breast Neoplasms Deep Learning
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Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.

Scientific reports Dec 28, 2024
This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breas...
Ultrasonography, Mammary Radiomics Breast Middle Aged Deep Learning Humans Aged Ultrasonography Female Adult ROC Curve Breast Neoplasms Diagnosis, Differential
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A feature fusion method based on radiomic features and revised deep features for improving tumor prediction in ultrasound images.

Computers in biology and medicine Dec 24, 2024
BACKGROUND: Radiomic features and deep features are both vitally helpful for the accurate prediction of tumor information in breast ultrasound. However, whether integrating radiomic features and deep features can improve the prediction performance of...
Radiomics Neural Networks, Computer Deep Learning Female Ultrasonography Humans ROC Curve Breast Neoplasms Ultrasonography, Mammary Image Interpretation, Computer-Assisted
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Interactively Fusing Global and Local Features for Benign and Malignant Classification of Breast Ultrasound Images.

Ultrasound in medicine & biology Dec 20, 2024
OBJECTIVE: Breast ultrasound (BUS) is used to classify benign and malignant breast tumors, and its automatic classification can reduce subjectivity. However, current convolutional neural networks (CNNs) face challenges in capturing global features, w...
Breast Image Interpretation, Computer-Assisted Breast Neoplasms Deep Learning Humans Ultrasonography, Mammary Neural Networks, Computer Female
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Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.

Ultrasound in medicine & biology Dec 17, 2024
OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast can...
Breast Image Interpretation, Computer-Assisted Multimodal Imaging Ultrasonography, Mammary Humans Female Breast Neoplasms Deep Learning
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CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.

Tomography (Ann Arbor, Mich.) Dec 12, 2024
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagn...
Breast Humans Mammography Neural Networks, Computer Female Multimodal Imaging Machine Learning Breast Neoplasms Ultrasonography, Mammary Early Detection of Cancer
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