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A semi-automated annotation algorithm based on weakly supervised learning for medical images

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Języki publikacji
EN
Abstrakty
EN
For medical image recognition, deep learning requires a massive training set, while anno-tation work is a tedious and time-consuming process because of the high technical thresh-old. Furthermore, it is difficult to guarantee annotation accuracy due to the knowledge, skills, and status of the annotator. In this research, we propose a semi-automated annota- tion model based on weakly supervised learning. Moreover, a target-level annotation method is proposed based on weakly supervised learning that is guided by machine learning. The machine learning method is used to screen the regions of interest (RoIs), whose semantic feature vectors are extracted by the deep learning method. Then, the machine learning method is used to cluster them, and the RoIs are finally classified and labeled by a distance comparison. Therefore, this model achieves target-level semi-auto- mated annotation by only using image-level annotations. We applied this method to ultrasound imaging of thyroid papillary carcinoma. The experiments demonstrate the potential of this new methodology to reduce the workload for pathologists and increase the objectivity of diagnoses. We find that 89.8% of papillary thyroid carcinoma regions can be detected automatically, while 82.6% of benign and normal tissue can be excluded without the use of any additional immunohistochemical markers or human intervention.
Twórcy
autor
  • College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, Guangdong, China
autor
  • College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, Guangdong, China
autor
  • College of Information Science and Technology/College of Cyber Security, Jinan University, Guangzhou, Guangdong, China
autor
  • Sun Yat-sen University Cancer Center, Guanzhou, China
autor
  • College of Mathematics and Informatics, South China Agricultural University, Guanzhou, China
  • College of Mathematics and Informatics, South China Agricultural University, Guanzhou, China
autor
  • College of Information Science and Technology/College of Cyber Security, Jinan University, No.601, West Huangpu Avenue, Guangzhou, Guangdong, China
Bibliografia
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  • [6] Faust O, Acharya UR, Meiburger KM, Molinari F, Ng KH. Comparative assessment of texture features for the identification of cancer in ultrasound images: a review. Biocybern Biomed Eng 2018;38(2).
  • [7] Li H, Weng J, Shi Y, Gu W, Mao Y, Wang Y, et al. An improved deep learning approach for detection of thyroid papillary cancer in ultrasound images. Sci Rep 2018;8(1):1–12.
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Uwagi
PL
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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