Semi-Automatic Segmentation of Breast Masses in Mammogram Images

Saleck, Moustapha Mohamed; Moutaouakkil, Abdelmajid El; R’Mili, Mohamed

doi:10.1145/3243250.3243264

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Semi-automatic segmentation algorithm also attracted much attention in the field of breast cancer image analysis, mainly applied to X-ray [80], ultrasound [81], MRI [82], and other images, and there is less research on pathological images of breast cancer. In recent related research, Lai et al [83], in conjunction with semi-supervised and active learning, proposed a segmentation algorithm for brain tissue pathological images and achieved IoU scores competitive with fully supervised learning.…”

Section: Segmentation Methods Of Breast Pathological Imagementioning

confidence: 99%

“…This is also used by most researchers to build the global features of breast cancer pathological images so as to improve the model performance. A large number of experimental results show the effectiveness of transformer architecture in this field [77,79,80]. The semi-automatic segmentation algorithm is relatively less used in the breast cancer pathological image segmentation task.…”

Section: Segmentation Methods Of Breast Pathological Imagementioning

confidence: 99%

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Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Zhao

Zhang

et al. 2022

Micromachines

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With the development of artificial intelligence technology and computer hardware functions, deep learning algorithms have become a powerful auxiliary tool for medical image analysis. This study was an attempt to use statistical methods to analyze studies related to the detection, segmentation, and classification of breast cancer in pathological images. After an analysis of 107 articles on the application of deep learning to pathological images of breast cancer, this study is divided into three directions based on the types of results they report: detection, segmentation, and classification. We introduced and analyzed models that performed well in these three directions and summarized the related work from recent years. Based on the results obtained, the significant ability of deep learning in the application of breast cancer pathological images can be recognized. Furthermore, in the classification and detection of pathological images of breast cancer, the accuracy of deep learning algorithms has surpassed that of pathologists in certain circumstances. Our study provides a comprehensive review of the development of breast cancer pathological imaging-related research and provides reliable recommendations for the structure of deep learning network models in different application scenarios.

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Section: Segmentation Methods Of Breast Pathological Imagementioning

confidence: 99%

Section: Segmentation Methods Of Breast Pathological Imagementioning

confidence: 99%

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Zhao

Zhang

et al. 2022

Micromachines

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“…Gu et al [16] achieved comparatively high precision on breast mass segmentation by means of a superpixel generation and curve evolution method. Given the region of interest selected by an expert, Saleck et al [17] developed a semi-automatic method of stepwise boundaries refinement in three stages for breast mass segmentation. Unfortunately, the aforementioned methods are mostly reliant on certain low-level image features extracted from the mass images, e.g.…”

mentioning

confidence: 99%

A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

Chen

Wang

et al. 2020

IEEE Access

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With the constant changes of people's lifestyle and living environment, the morbidity of breast cancer is increasing year by year. It is highly imperative to develop an effective breast mass segmentation method for early breast cancer diagnosis. However, segmenting breast masses in mammograms is still one hot issue with enormous challenges because of masses' irregular shapes and various sizes. In this study, we propose multi-adversarial learning to capture multi-scale image information for accurate breast mass segmentation. To effectively reinforce higher-order consistency in the segmentation results, the proposed network introduces the idea of adversarial networks, mainly consisting of a segmentation network and a discrimination network. An improved U-Net is introduced as the segmentation network to generate masks of the suspicious regions, while the discrimination network combines three convolutional critic networks that operate at different scales to discriminate the input masks. To weaken the unbalanced class problem and produce fine-grained segmentation results, weighted cross entropy loss and Earth-Mover distance are jointly used as an integrated loss function to guide the optimization process. Furthermore, the spectral normalization is adopted to the critics to alleviate the instability of training. The effectiveness of the proposed method is evaluated on two public datasets (INbreast and CBIS-DDSM). Experimental results empirically demonstrate that our method outperforms FCNs-based methods and the state-of-the-art method with dice of 81.64% for INbreast and 82.16% for CBIS-DDSM.

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LMA-Net: A lesion morphology aware network for medical image segmentation towards breast tumors

Peng

Zhang²,

Meng³

et al. 2022

Computers in Biology and Medicine

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Semi-Automatic Segmentation of Breast Masses in Mammogram Images

Cited by 3 publications

References 17 publications

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

Application of Deep Learning in Histopathology Images of Breast Cancer: A Review

A Novel Multi-Scale Adversarial Networks for Precise Segmentation of X-Ray Breast Mass

LMA-Net: A lesion morphology aware network for medical image segmentation towards breast tumors

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