Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints

Li, Yongshuai; Liu, Yuan; Huang, Lijie; Wang, Zhili; Luo, Jianwen

doi:10.1016/j.media.2021.102315

Cited by 29 publications

(6 citation statements)

References 19 publications

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“…It is perhaps because of the various imaging methods for breast cancer. AI is widely used in mammography ( 31 , 32 ), magnetic resonance imaging ( 33 , 34 ), ultrasound ( 35 , 36 ), and histopathology ( 37 ) for tumor classification and segmentation. For instance, Tanaka H. et al.…”

Section: Discussionmentioning

confidence: 99%

Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022

et al. 2023

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BackgroundArtificial intelligence (AI) is widely applied in cancer field nowadays. The aim of this study is to explore the hotspots and trends of AI in cancer research.MethodsThe retrieval term includes four topic words (“tumor,” “cancer,” “carcinoma,” and “artificial intelligence”), which were searched in the database of Web of Science from January 1983 to December 2022. Then, we documented and processed all data, including the country, continent, Journal Impact Factor, and so on using the bibliometric software.ResultsA total of 6,920 papers were collected and analyzed. We presented the annual publications and citations, most productive countries/regions, most influential scholars, the collaborations of journals and institutions, and research focus and hotspots in AI-based cancer research.ConclusionThis study systematically summarizes the current research overview of AI in cancer research so as to lay the foundation for future research.

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Section: Discussionmentioning

confidence: 99%

Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022

et al. 2023

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“…Initially, the segmentation model was trained with labeled data; subsequently, unlabeled data was fed into the segmentation model to obtain predictions as pseudo-labels, which were used to retrain the network for updating parameters. Li et al [45] designed a semi-supervised segmentation network based on the Temporal Ensembling (TE) method. After each training, the current model made predictions for the entire training set to generate pseudo-labels for each sample.…”

Section: Pseudo-labels-based Suvosmentioning

confidence: 99%

Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review

Jin,

Zhaojun,

Yanping

2023

Advanced Ultrasound in Diagnosis and Therapy

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Real-time intelligent segmentation of ultrasound video object is a demanding task in the field of medical image processing and serves as an essential and critical step in image-guided clinical procedures. However, obtaining reliable and accurate medical image annotations often necessitates expert guidance, making the acquisition of large-scale annotated datasets challenging and costly. This presents obstacles for traditional supervised learning methods. Consequently, semi-supervised learning (SSL) has emerged as a promising solution, capable of utilizing unlabeled data to enhance model performance and has been widely adopted in medical image segmentation tasks. However, striking a balance between segmentation accuracy and inference speed remains a challenge for real-time segmentation. This paper provides a comprehensive review of research progress in real-time intelligent semi-supervised ultrasound video object segmentation (SUVOS) and offers insights into future developments in this area.

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“…High diagnostic rates signify AI's effectiveness in correctly diagnosing conditions based on imaging data, reducing errors and improving patient outcomes. AI's integration into medical imaging thus represents a significant advancement in diagnostic techniques, offering more accurate, efficient, and reliable evaluations [3][4][5][6][7][8]. Additionally, AI has been found to be useful for diagnosing axillary lymph node metastasis and predicting lymph node metastasis in breast cancer [9,10].…”

Section: Introductionmentioning

confidence: 99%

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

Zama,

Fujioka,

Yamaga

et al. 2023

Medicina

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Background and Objectives: This study compares the clinical properties of original breast ultrasound images and those synthesized by a generative adversarial network (GAN) to assess the clinical usefulness of GAN-synthesized images. Materials and Methods: We retrospectively collected approximately 200 breast ultrasound images for each of five representative histological tissue types (cyst, fibroadenoma, scirrhous, solid, and tubule-forming invasive ductal carcinomas) as training images. A deep convolutional GAN (DCGAN) image-generation model synthesized images of the five histological types. Two diagnostic radiologists (reader 1 with 13 years of experience and reader 2 with 7 years of experience) were given a reading test consisting of 50 synthesized and 50 original images (≥1-month interval between sets) to assign the perceived histological tissue type. The percentages of correct diagnoses were calculated, and the reader agreement was assessed using the kappa coefficient. Results: The synthetic and original images were indistinguishable. The correct diagnostic rates from the synthetic images for readers 1 and 2 were 86.0% and 78.0% and from the original images were 88.0% and 78.0%, respectively. The kappa values were 0.625 and 0.650 for the synthetic and original images, respectively. The diagnoses made from the DCGAN synthetic images and original images were similar. Conclusion: The DCGAN-synthesized images closely resemble the original ultrasound images in clinical characteristics, suggesting their potential utility in clinical education and training, particularly for enhancing diagnostic skills in breast ultrasound imaging.

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Deep weakly-supervised breast tumor segmentation in ultrasound images with explicit anatomical constraints

Cited by 29 publications

References 19 publications

Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022

Global development of artificial intelligence in cancer field: a bibliometric analysis range from 1983 to 2022

Semi-supervised Learning for Real-time Segmentation of Ultrasound Video Objects: A Review

Clinical Utility of Breast Ultrasound Images Synthesized by a Generative Adversarial Network

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