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Tsinghua Science and Technology 2024, 29(5): 1524-1539 https://doi.org/10.26599/TST.2023.9010126
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Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

Show Author's Information Ling Tan1,  Ying Liang1,  Jingming Xia2( ),  Hui Wu1,  Jining Zhu1
School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China
Keywords:
deep learning, mammography diagnosis, mass detection, cross-layer attention, adaptive positive sample selection
Cite this article:
Tan L, Liang Y, Xia J, et al. Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks. Tsinghua Science and Technology, 2024, 29(5): 1524-1539. https://doi.org/10.26599/TST.2023.9010126
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Abstract Full text About this article

Breast mass identification is of great significance for early screening of breast cancer, while the existing detection methods have high missed and misdiagnosis rate for small masses. We propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once (RCM-YOLO), which improves the identifiability of small masses by increasing the resolution of feature maps, adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters, and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature locations. In the training process, we propose an adaptive positive sample selection algorithm to automatically select positive samples, which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the model. To verify the performance of our model, we used public datasets to carry out the experiments. The results showed that the mean Average Precision (mAP) of RCM-YOLO reached 90.34%, compared with YOLOv5, the missed detection rate for small masses of RCM-YOLO was reduced to 11%, and the single detection time was reduced to 28 ms. The detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between features. Our method can help doctors in batch screening of breast images, and significantly promote the detection rate of small masses and reduce misdiagnosis.


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About this article

Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks

Show Author's information Ling Tan1, Ying Liang1, Jingming Xia2( ), Hui Wu1, Jining Zhu1
School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China
School of Artificial Intelligence, Nanjing University of Information Science & Technology, Nanjing 210044, China

Abstract

Breast mass identification is of great significance for early screening of breast cancer, while the existing detection methods have high missed and misdiagnosis rate for small masses. We propose a small target breast mass detection network named Residual asymmetric dilated convolution-Cross layer attention-Mean standard deviation adaptive selection-You Only Look Once (RCM-YOLO), which improves the identifiability of small masses by increasing the resolution of feature maps, adopts residual asymmetric dilated convolution to expand the receptive field and optimize the amount of parameters, and proposes the cross-layer attention that transfers the deep semantic information to the shallow layer as auxiliary information to obtain key feature locations. In the training process, we propose an adaptive positive sample selection algorithm to automatically select positive samples, which considers the statistical features of the intersection over union sets to ensure the validity of the training set and the detection accuracy of the model. To verify the performance of our model, we used public datasets to carry out the experiments. The results showed that the mean Average Precision (mAP) of RCM-YOLO reached 90.34%, compared with YOLOv5, the missed detection rate for small masses of RCM-YOLO was reduced to 11%, and the single detection time was reduced to 28 ms. The detection accuracy and speed can be effectively improved by strengthening the feature expression of small masses and the relationship between features. Our method can help doctors in batch screening of breast images, and significantly promote the detection rate of small masses and reduce misdiagnosis.

Keywords: deep learning, mammography diagnosis, mass detection, cross-layer attention, adaptive positive sample selection

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Received: 11 April 2023
Revised: 26 July 2023
Accepted: 17 October 2023
Published: 02 May 2024
Issue date: October 2024

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© The Author(s) 2024.

Acknowledgements

Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. 62271264), the National Key Research and Development Program of China (No. 2021ZD0102100), and the Industry University Research Foundation of Jiangsu Province (No. BY2022459).

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