Purpose: To develop a novel BI-RADS features oriented triplet path networks, namely BIRADS-TPN, for accurate breast ultrasound (US) computer-aided diagnosis (CAD).
Methods: The proposed BIRADS-TPN is designed to incorporate clinical-approved breast lesion characteristics (BI-RADS features) and a triplet path networks with Fisher discriminant analysis (TPN) to achieve accurate diagnosis on US images with small training dataset. Original breast US images are converted to BIRADS-oriented feature maps (BFMs) using a distance-transformation coupled Gaussian filter. Then the converted BFMs are used as the input of TPN algorithm, which is characterized as Fisher discriminant analysis guided two unsupervised stacked convolutional Auto-Encoder (SCAE) networks for benign and malignant image reconstruction independently. The developed BIRADS-TPN algorithm is trained with an alternative learning strategy by balancing reconstruction error and Fisher loss to ensure the representation and discriminancy of features from the shared sub-network. Original images based (ORI-) or BIRADS based (BIRADS-) TPN are compared to conventional neural network-based lesion classification using features from unconstrained SCAE. Networks are trained and tested on 295 patientsâ€™ ultrasound images (205 benign and 90 malignant) with 80% for training and the remaining for testing.
Results: The performance of trained networks are evaluated with metrics, including accuracy (ACC), area under receiver operating characteristic curve (AUC), sensitivity (SEN), specificity (SPE). Evaluation results show BIRADS-TPN achieved the best performance among comparison with ACC and AUC ~83 and 76%, respectively.
Conclusion: BIRADS-TPN achieved high accuracy on the testing dataset and ranked the best performance among four comparing networks, which indicated that BIRADS-TPN could a promising model for effective US breast lesion CAD using small datasets.
Funding Support, Disclosures, and Conflict of Interest: This work was supported by the Seed Grant from Department of Radiation Oncology at University of Texas Southwestern Medical Center.