Various deep learning methods have been proposed to segment breast lesions from ultrasound images. However, similar intensity distributions, variable tumor morphologies and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/CGPxy/AAU-net</uri> . 

/pdf/aau-net-an-adaptive-attention-u-net-for-breast-lesions-2l0h7uc7.pdf

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 and blurred boundaries present challenges for breast lesions segmentation, especially for malignant tumors with irregular shapes. Considering the complexity of ultrasound images, we develop an adaptive attention U-net (AAU-net) to segment breast lesions automatically and stably from ultrasound images. Specifically, we introduce a hybrid adaptive attention module (HAAM), which mainly consists of a channel self-attention block and a spatial self-attention block, to replace the traditional convolution operation. Compared with the conventional convolution operation, the design of the hybrid adaptive attention module can help us capture more features under different receptive fields. Different from existing attention mechanisms, the HAAM module can guide the network to adaptively select more robust representation in channel and space dimensions to cope with more complex breast lesions segmentation. Extensive experiments with several state-of-the-art deep learning segmentation methods on three public breast ultrasound datasets show that our method has better performance on breast lesions segmentation. Furthermore, robustness analysis and external experiments demonstrate that our proposed AAU-net has better generalization performance in the breast lesion segmentation. Moreover, the HAAM module can be flexibly applied to existing network frameworks. The source code is available on https://github.com/CGPxy/AAU-net.

Abstract Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning. 

/pdf/on-the-analyses-of-medical-images-using-traditional-machine-2rw8bs50.pdf

On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Deep Learning-based Multi-stage Segmentation Method Using Ultrasound Images for Breast Cancer Diagnosis

Accurate lesion segmentation is a critical technology basis for the treatment and prognosis of stroke. Stroke lesion segmentation suffers from complex background and noise interferes due to the similar grayscale characteristics of tissue structures and imaging mode in medical image. To resolve the difficulty of fuzzy boundary for lesion segmentation, we propose a novel two-stage brain MRI lesion segmentation method, called W-Net. W-Net leverages the CNN and transformer-based method as the backbone network, and introduces boundary deformation module (BDM), boundary constraint module (BCM) to handle the tough condition of fuzzy boundary. The BDM utilizes circular convolution to correct the initial boundary and BCM exploits dilated convolution to dynamically constrain the object boundary. Moreover, we design a multi-task learning loss function to optimize the W-Net from both region and boundary perspectives. Extensive experiments are conducted using the two ischemic stroke lesion segmentation datasets, and the results are compared with other advanced models. The experimental results demonstrate that the proposed method is effective in preserving the edge information of stroke lesions, thus achieving competitive performance on the segmentation task. 

W-Net: A boundary-enhanced segmentation network for stroke lesions

Boundary-rendering network for breast lesion segmentation in ultrasound images

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Boundary-rendering network for breast lesion segmentation in ultrasound images