TY - JOUR
T1 - Incorporating Tumor Edge Information for Fine-Grained BI-RADS Classification of Breast Ultrasound Images
AU - Xu, Meng
AU - Huang, Jianhua
AU - Huang, Kuan
AU - Liu, Feifei
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2024
Y1 - 2024
N2 - Breast Ultrasound (BUS) imaging is an essential tool for the early detection of breast cancer. The Breast Imaging Reporting and Data System (BI-RADS) in BUS images helps standardize the interpretation and reporting process by categorizing breast tumors into multiple classes, which enables radiologists to make more accurate diagnoses and treatment plans. However, most existing classification methods distinguish only between benign and malignant categories. In addition, features extracted by classic convolutional neural networks tend to be insufficient when subdividing BUS images into fine-grained BI-RADS classes, as they typically do not consider prior knowledge in medical applications, such as foreground shape. To address the above problems, we propose a novel fine-grained BI-RADS classification approach that integrates tumor edges to provide more efficient discriminative features. Firstly, weakly supervised pseudo-label generation: we detect coarse tumor edge regions utilizing a pre-trained PiDiNet and two novel loss functions based on prior knowledge from our dataset. The detected tumor edges are subsequently used as pseudo-labels for the next step. Secondly, co-training a tumor edge detection network and a BI-RADS classification network: edge images generated by the edge detection network are used as weight masks to highlight tumor edge regions as discriminative parts for better classification results, especially for categories with high similarities. The proposed method is evaluated on a BUS image dataset of 1061 images with BI-RADS categories. Experimental results indicate that the proposed method significantly improves over the baseline model by 4.73% in terms of top-1 accuracy.
AB - Breast Ultrasound (BUS) imaging is an essential tool for the early detection of breast cancer. The Breast Imaging Reporting and Data System (BI-RADS) in BUS images helps standardize the interpretation and reporting process by categorizing breast tumors into multiple classes, which enables radiologists to make more accurate diagnoses and treatment plans. However, most existing classification methods distinguish only between benign and malignant categories. In addition, features extracted by classic convolutional neural networks tend to be insufficient when subdividing BUS images into fine-grained BI-RADS classes, as they typically do not consider prior knowledge in medical applications, such as foreground shape. To address the above problems, we propose a novel fine-grained BI-RADS classification approach that integrates tumor edges to provide more efficient discriminative features. Firstly, weakly supervised pseudo-label generation: we detect coarse tumor edge regions utilizing a pre-trained PiDiNet and two novel loss functions based on prior knowledge from our dataset. The detected tumor edges are subsequently used as pseudo-labels for the next step. Secondly, co-training a tumor edge detection network and a BI-RADS classification network: edge images generated by the edge detection network are used as weight masks to highlight tumor edge regions as discriminative parts for better classification results, especially for categories with high similarities. The proposed method is evaluated on a BUS image dataset of 1061 images with BI-RADS categories. Experimental results indicate that the proposed method significantly improves over the baseline model by 4.73% in terms of top-1 accuracy.
KW - BI-RADS classification
KW - breast ultrasound
KW - deep learning
KW - edge detection
KW - Weakly supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85188014348&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2024.3374380
DO - 10.1109/ACCESS.2024.3374380
M3 - Article
AN - SCOPUS:85188014348
SN - 2169-3536
VL - 12
SP - 38732
EP - 38744
JO - IEEE Access
JF - IEEE Access
ER -