Affiliation:
1. School of Health Science and Engineering University of Shanghai for Science and Technology Shanghai China
2. Department of Radiology Ruijin Hospital Affiliated to Shanghai Jiaotong University School of Medicine Shanghai China
3. Medical Imaging Center Taian Center Hospital Shandong China
Abstract
AbstractBackgroundBreast cancer is a typically diagnosed and life‐threatening cancer in women. Thus, dynamic contrast‐enhanced magnetic resonance imaging (DCE‐MRI) is increasingly used for breast lesion detection and diagnosis because of the high resolution of soft tissues. Moreover, supervised detection methods have been implemented for breast lesion detection. However, these methods require substantial time and specialized staff to develop the labeled training samples.PurposeTo investigate the potential of weakly supervised deep learning models for breast lesion detection.MethodsA total of 1003 breast DCE‐MRI studies were collected, including 603 abnormal cases with 770 breast lesions and 400 normal subjects. The proposed model was trained using breast DCE‐MRI considering only the image‐level labels (normal and abnormal) and optimized for classification and detection sub‐tasks simultaneously. Ablation experiments were performed to evaluate different convolutional neural network (CNN) backbones (VGG19 and ResNet50) as shared convolutional layers, as well as to evaluate the effect of the preprocessing methods.ResultsOur weakly supervised model performed better with VGG19 than with ResNet50 (p < 0.05). The average precision (AP) of the classification sub‐task was 91.7% for abnormal cases and 88.0% for normal samples. The area under the receiver operating characteristic (ROC) curve (AUC) was 0.939 (95% confidence interval [CI]: 0.920–0.941). The weakly supervised detection task AP was 85.7%, and the correct location (CorLoc) was 90.2%. A sensitivity of 84.0% at two‐false positives per image was assessed based on free‐response ROC (FROC) curve.ConclusionsThe results confirm that a weakly supervised CNN based on self‐transfer learning is an effective and promising auxiliary tool for detecting breast lesions.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Shanghai
Cited by
2 articles.
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1. Applications of Deep Learning to Magnetic Resonance Imaging (MRI);2023 International Conference on Computing, Electronics & Communications Engineering (iCCECE);2023-08-14
2. Weakly Supervised Breast Lesion Detection in Dynamic Contrast-Enhanced MRI;Journal of Digital Imaging;2023-05-30