Towards clinical application of artificial intelligence in ultrasound imaging
Artificial intelligence (AI) is being increasingly adopted in medical research and applications.
Medical AI devices have continuously been approved by the Food and Drug Administration …
Medical AI devices have continuously been approved by the Food and Drug Administration …
Deep learning in breast cancer imaging: A decade of progress and future directions
Breast cancer has reached the highest incidence rate worldwide among all malignancies
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
since 2020. Breast imaging plays a significant role in early diagnosis and intervention to …
[HTML][HTML] Unified focal loss: Generalising dice and cross entropy-based losses to handle class imbalanced medical image segmentation
Automatic segmentation methods are an important advancement in medical image analysis.
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
Machine learning techniques, and deep neural networks in particular, are the state-of-the-art …
AAU-net: an adaptive attention U-net for breast lesions segmentation in ultrasound images
Various deep learning methods have been proposed to segment breast lesions from
ultrasound images. However, similar intensity distributions, variable tumor morphologies …
ultrasound images. However, similar intensity distributions, variable tumor morphologies …
CSwin-PNet: A CNN-Swin Transformer combined pyramid network for breast lesion segmentation in ultrasound images
H Yang, D Yang - Expert Systems with Applications, 2023 - Elsevier
Currently, the automatic segmentation of breast tumors based on breast ultrasound (BUS)
images is still a challenging task. Most lesion segmentation methods are implemented …
images is still a challenging task. Most lesion segmentation methods are implemented …
Attention gate resU-Net for automatic MRI brain tumor segmentation
Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
treatment of MRI brain tumors. It helps doctors to locate and measure tumors, as well as …
SMU-Net: Saliency-guided morphology-aware U-Net for breast lesion segmentation in ultrasound image
Z Ning, S Zhong, Q Feng, W Chen… - IEEE transactions on …, 2021 - ieeexplore.ieee.org
Deep learning methods, especially convolutional neural networks, have been successfully
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …
applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern …
C-Net: Cascaded convolutional neural network with global guidance and refinement residuals for breast ultrasound images segmentation
Background and objective Breast lesions segmentation is an important step of computer-
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
aided diagnosis system. However, speckle noise, heterogeneous structure, and similar …
Rethinking the unpretentious U-net for medical ultrasound image segmentation
Breast tumor segmentation from ultrasound images is one of the key steps that help us
characterize and localize tumor regions. However, variable tumor morphology, blurred …
characterize and localize tumor regions. However, variable tumor morphology, blurred …
A comparative study of pre-trained convolutional neural networks for semantic segmentation of breast tumors in ultrasound
W Gómez-Flores… - Computers in Biology and …, 2020 - Elsevier
The automatic segmentation of breast tumors in ultrasound (BUS) has recently been
addressed using convolutional neural networks (CNN). These CNN-based approaches …
addressed using convolutional neural networks (CNN). These CNN-based approaches …