AIMC Topic: Ultrasonography

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ATTransUNet: An enhanced hybrid transformer architecture for ultrasound and histopathology image segmentation.

Computers in biology and medicine
Recently, researchers have introduced Transformer into medical image segmentation networks to encode long-range dependency, which makes up for the deficiencies of convolutional neural networks (CNNs) in global context modeling, and thus improves segm...

Unsupervised landmark detection and classification of lung infection using transporter neural networks.

Computers in biology and medicine
Supervised deep learning techniques have been very popular in medical imaging for various tasks of classification, segmentation, and object detection. However, they require a large number of labelled data which is expensive and requires many hours of...

Deep learning model based on contrast-enhanced ultrasound for predicting early recurrence after thermal ablation of colorectal cancer liver metastasis.

European radiology
OBJECTIVES: To develop and validate a deep learning (DL) model based on quantitative analysis of contrast-enhanced ultrasound (CEUS) images that predicts early recurrence (ER) after thermal ablation (TA) of colorectal cancer liver metastasis (CRLM).

Quantifying Valve Regurgitation Using 3-D Doppler Ultrasound Images and Deep Learning.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Accurate quantification of cardiac valve regurgitation jets is fundamental for guiding treatment. Cardiac ultrasound is the preferred diagnostic tool, but current methods for measuring the regurgitant volume (RVol) are limited by low accuracy and hig...

Ultrasound Imaging With a Flexible Probe Based on Element Array Geometry Estimation Using Deep Neural Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Conventionally, ultrasound (US) diagnosis is performed using hand-held rigid probes. Such devices are difficult to be used for long-term monitoring because they need to be continuously pressed against the body to remove the air between the probe and ...

Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.

European radiology
OBJECTIVES: To establish a breast lesion risk stratification system using ultrasound images to predict breast malignancy and assess Breast Imaging Reporting and Data System (BI-RADS) categories simultaneously.

Intravascular Tracking of Micro-Agents Using Medical Ultrasound: Towards Clinical Applications.

IEEE transactions on bio-medical engineering
OBJECTIVE: This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clinically-relevant scenarios.

An integrated deep learning model for the prediction of pathological complete response to neoadjuvant chemotherapy with serial ultrasonography in breast cancer patients: a multicentre, retrospective study.

Breast cancer research : BCR
BACKGROUND: The biological phenotype of tumours evolves during neoadjuvant chemotherapy (NAC). Accurate prediction of pathological complete response (pCR) to NAC in the early-stage or posttreatment can optimize treatment strategies or improve the bre...

Deep learning for hetero-homo conversion in channel-domain for phase aberration correction in ultrasound imaging.

Ultrasonics
Echo imaging in ultrasound computed tomography (USCT) using the synthetic aperture technique is performed with the assumption that the speed of sound is constant in the system. However, tissue heterogeneity causes a mismatch between the predicted arr...

Deep learning radiomics of ultrasonography for comprehensively predicting tumor and axillary lymph node status after neoadjuvant chemotherapy in breast cancer patients: A multicenter study.

Cancer
BACKGROUND: Neoadjuvant chemotherapy (NAC) can downstage tumors and axillary lymph nodes in breast cancer (BC) patients. However, tumors and axillary response to NAC are not parallel and vary among patients. This study aims to explore the feasibility...