[1]MO Hongwei,SUN Qi,SUN Peng,et al.Self-supervised pretraining detection of mammographic mass targets in breast[J].CAAI Transactions on Intelligent Systems,2024,19(5):1082-1091.[doi:10.11992/tis.202304032]
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Self-supervised pretraining detection of mammographic mass targets in breast

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 5 Page number: 1082-1091 Column: 学术论文—机器学习 Public date: 2024-09-05
Title:
Self-supervised pretraining detection of mammographic mass targets in breast
Author(s):
MO Hongwei1;  SUN Qi1;  SUN Peng1;  ZHANG Xianyu2;  SUN Jianghong3;  SUN Weijia3
1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. Breast Surgery Department, Cancer Hospital Affiliated to Harbin Medical University, Harbin 150001, China;
3. Imaging Center, Cancer Hospital Affiliated to Harbin Medical University, Harbin 150001, China
Keywords:
object detection; self-supervised; mammographic; pretraining; data augment; visual representation; convolution neural network; image classification
CLC:
TP181
DOI:
10.11992/tis.202304032
Abstract:
Deep learning technology has become a major research focus in assisting doctors with breast cancer diagnosis, particularly in mammographic imaging. This research encompasses tasks such as benign and malignant classification, lesion region detection, and lesion region segmentation. However, the performance of deep learning models heavily relies on large amounts of labeled data. Medical image datasets often suffer from limited data, high labeling costs, and poor labeling quality of open datasets, which make it challenging to apply deep learning technology effectively in the medical imaging field. To advance computer-aided diagnosis of breast mammographic targets using deep learning without relying on large amounts of labeled data, this study proposes a method for self-supervised target detection in mammographic images. The method addresses the task of detecting breast mammographic masses. Extensive data from cancer hospitals were pretrained, fine-tuned, and tested on the open dataset DDSM. The experimental results show that the proposed model performs excellently in detecting breast mammographic masses and does not rely on location tags. These results highlight the significant research value of the model and promising application prospects.
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