AIMC Topic: Ultrasonography, Mammary

Clear Filters Showing 201 to 210 of 227 articles

FedBCD: Federated Ultrasound Video and Image Joint Learning for Breast Cancer Diagnosis.

IEEE transactions on medical imaging
Ultrasonography plays an essential role in breast cancer diagnosis. Current deep learning based studies train the models on either images or videos in a centralized learning manner, lacking consideration of joint benefits between two different modali...

Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features.

Breast cancer research and treatment
PURPOSE: To establish a reliable machine learning model to predict malignancy in breast lesions identified by ultrasound (US) and optimize the negative predictive value to minimize unnecessary biopsies.

An explainable AI-driven deep neural network for accurate breast cancer detection from histopathological and ultrasound images.

Scientific reports
Breast cancer represents a significant global health challenge, which makes it essential to detect breast cancer early and accurately to improve patient prognosis and reduce mortality rates. However, traditional diagnostic processes relying on manual...

Optimizing breast lesions diagnosis and decision-making with a deep learning fusion model integrating ultrasound and mammography: a dual-center retrospective study.

Breast cancer research : BCR
BACKGROUND: This study aimed to develop a BI-RADS network (DL-UM) via integrating ultrasound (US) and mammography (MG) images and explore its performance in improving breast lesion diagnosis and management when collaborating with radiologists, partic...

Hierarchical diagnosis of breast phyllodes tumors enabled by deep learning of ultrasound images: a retrospective multi-center study.

Cancer imaging : the official publication of the International Cancer Imaging Society
OBJECTIVE: Phyllodes tumors (PTs) are rare breast tumors with high recurrence rates, current methods relying on post-resection pathology often delay detection and require further surgery. We propose a deep-learning-based Phyllodes Tumors Hierarchical...

Deep learning-assisted distinguishing breast phyllodes tumours from fibroadenomas based on ultrasound images: a diagnostic study.

The British journal of radiology
OBJECTIVES: To evaluate the performance of ultrasound-based deep learning (DL) models in distinguishing breast phyllodes tumours (PTs) from fibroadenomas (FAs) and their clinical utility in assisting radiologists with varying diagnostic experiences.

Weakly Supervised Breast Ultrasound Image Segmentation Based on Image Selection.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic segmentation in Breast Ultrasound (BUS) imaging is vital to BUS computer-aided diagnostic systems. Fully supervised learning approaches can attain high accuracy, yet they depend on pixel-level annotations that are challenging to obtain. As ...

A comparison between Deep Learning architectures for the assessment of breast tumor segmentation using VSI ultrasound protocol.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Automatic breast tumor ultrasound segmentation is one of the most critical components in the development of tools for breast cancer diagnosis. Several deep learning algorithms have been tested with public and private datasets but none of them has bee...

Screening Outcomes of Mammography with AI in Dense Breasts: A Comparative Study with Supplemental Screening US.

Radiology
Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear. Purpose To compare the performance of mammography alone, mammography with AI, and mam...