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Breast Cancer Detection of Small Sample Based on Data Augmentation and Corner Pooling

Published: 17 December 2020 Publication History

Abstract

Breast cancer is the most common cancer among women worldwide. The effective detection the location of breast cancer from the ultrasound images can assist doctors in diagnosing breast cancer. Diverse morphology, blurred edges, and small amount of data causes great difficulty in the detection of breast cancer. Deep learning is very advantageous when facing these problems. However, the problems of training on small sample datasets and the imbalance of positive and negative samples are problems that need to be solved. In order to improve the accuracy of ultrasound breast cancer detection, a small sample breast cancer detection method based on data augmentation and corner pooling is proposed in this paper. In this method, we propose a way for solving over-fitting of small samples and solving the imbalance problem of positive and negative samples. Data augmentation module based on geometric and noise transformation is proposed to solve the problem of small samples, and detection module based on focal loss and corner pooling is proposed to solve the problem of imbalance samples. The experiment found that the method used in this paper has more advantages than the mainstream methods in difficult to distinguish samples. The method used in this paper has an AP of 84.65%, which is higher than state-of-the-art methods.

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Cited By

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  • (2024) VSG 3 A 2 : A Genetic Algorithm-Based Virtual Sample Generation Approach Using Information Gain and Acceptance-Rejection Sampling IEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.329870328:5(1514-1528)Online publication date: Oct-2024
  • (2022)Classification Performance of Breast Tumors Using Deep Learning by a Reflected Pixel-Based Augmentation Method2022 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)10.1109/WIECON-ECE57977.2022.10151314(254-258)Online publication date: 30-Dec-2022

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cover image ACM Other conferences
SSPS '20: Proceedings of the 2020 2nd Symposium on Signal Processing Systems
July 2020
125 pages
ISBN:9781450388627
DOI:10.1145/3421515
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 December 2020

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Author Tags

  1. B-scans ultrasound
  2. Breast cancer
  3. Deep learning
  4. Object detection
  5. Small sample learning

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • the key-area research an development program of guangdong province
  • Program of Guangdong Special Funds
  • Guangdong Province Science and Technology Project
  • R & D projects in key areas of Guangdong Province
  • the ?Blue Fire Plan? (Huizhou) Industry-University-Research
  • the Key Program of NSFC-Guangdong Joint Funds
  • Supported by Guangdong Provincial Key Laboratory of Cyber-Physical System

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Cited By

View all
  • (2024) VSG 3 A 2 : A Genetic Algorithm-Based Virtual Sample Generation Approach Using Information Gain and Acceptance-Rejection Sampling IEEE Transactions on Evolutionary Computation10.1109/TEVC.2023.329870328:5(1514-1528)Online publication date: Oct-2024
  • (2022)Classification Performance of Breast Tumors Using Deep Learning by a Reflected Pixel-Based Augmentation Method2022 IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE)10.1109/WIECON-ECE57977.2022.10151314(254-258)Online publication date: 30-Dec-2022

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