SHA-MTL: soft and hard attention multi-task learning for automated breast cancer ultrasound image segmentation and classification

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

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A Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images: A Review

Journal of Applied Science and Technology Trends ◽

10.38094/jastt20201328 ◽

2020 ◽

Vol 1 (3) ◽

pp. 78-91

Author(s):

Muhammad Muhammad ◽

Diyar Zeebaree ◽

Adnan Mohsin Abdulazeez Brifcani ◽

Jwan Saeed ◽

Dilovan Asaad Zebari

Keyword(s):

Breast Cancer ◽

Image Segmentation ◽

Signal To Noise Ratio ◽

Ultrasound Image ◽

Region Of Interest ◽

Speckle Noise ◽

Ultrasound Images ◽

Clustering Methods ◽

Advantages And Disadvantages ◽

Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

Download Full-text

A Review on Region of Interest Segmentation Based on Clustering Techniques for Breast Cancer Ultrasound Images

Journal of Applied Science and Technology Trends ◽

10.38094/2020jastt1328 ◽

2020 ◽

Vol 1 (3) ◽

pp. 78-91

Author(s):

Muhammad Muhammad ◽

Diyar Zeebaree ◽

Adnan Mohsin Abdulazeez Brifcani ◽

Jwan Saeed ◽

Dilovan Asaad Zebari

Keyword(s):

Breast Cancer ◽

Image Segmentation ◽

Signal To Noise Ratio ◽

Ultrasound Image ◽

Region Of Interest ◽

Speckle Noise ◽

Ultrasound Images ◽

Clustering Methods ◽

Advantages And Disadvantages ◽

Ultrasound Image Segmentation

The most prevalent cancer amongst women is woman breast cancer. Ultrasound imaging is a widely employed method for identifying and diagnosing breast abnormalities. Computer-aided diagnosis technologies have lately been developed with ultrasound images to help radiologists enhance the accuracy of the diagnosis. This paper presents several ultrasound image segmentation techniques, mainly focus on eight clustering methods over the last 10 years, and it shows the advantages and disadvantages of these approaches. Breast ultrasound image segmentation is, therefore, still an accessible and challenging issue due to numerous ultrasound artifacts introduced in the imaging process, including high speckle noise, poor contrast, blurry edges, weak signal-to-noise ratio, and intensity inhomogeneity.

Download Full-text