Grouped mask region convolution neural networks for improved breast cancer segmentation in mammography images

Prasad, Rajesh; Hashim, Ezzeddin Kamil Mohamed

doi:10.1007/s12530-023-09527-8

Cited by 3 publications

(1 citation statement)

References 31 publications

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“…The results of the study reported in [56] demonstrated highly accurate breast cancer segmentation by combining mask regional CNNs (mask R-CNNs) with group CNNs (G-CNNs). These approaches maximize the share of weights and the expressive capacity of the model while maintaining rotational invariance.…”

Section: Segmentation Of Breast Massesmentioning

confidence: 99%

A Review of Artificial Intelligence in Breast Imaging

Al-Karawi,

Al-Zaidi,

Helael

et al. 2024

Tomography

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With the increasing dominance of artificial intelligence (AI) techniques, the important prospects for their application have extended to various medical fields, including domains such as in vitro diagnosis, intelligent rehabilitation, medical imaging, and prognosis. Breast cancer is a common malignancy that critically affects women’s physical and mental health. Early breast cancer screening—through mammography, ultrasound, or magnetic resonance imaging (MRI)—can substantially improve the prognosis for breast cancer patients. AI applications have shown excellent performance in various image recognition tasks, and their use in breast cancer screening has been explored in numerous studies. This paper introduces relevant AI techniques and their applications in the field of medical imaging of the breast (mammography and ultrasound), specifically in terms of identifying, segmenting, and classifying lesions; assessing breast cancer risk; and improving image quality. Focusing on medical imaging for breast cancer, this paper also reviews related challenges and prospects for AI.

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Section: Segmentation Of Breast Massesmentioning

confidence: 99%