Extending CohereNet to Retain Physical Features when Classifying Benign or Malignant Breast Masses

Breast ultrasound is often used as a supplement to mammography. However, standard breast ultrasound imaging often has a high false positive rate which limits its use as a screening tool. The spatial coherence of ultrasound signals has particularly impactful implications for breast mass diagnoses when considering both quantitative ultrasound and coherence-based ultrasound beamforming techniques. However, coherence can be computationally expensive to compute. Our recent work demonstrated a novel deep neural network architecture, named CohereNet, which has the ability to estimate coherence features by leveraging the universal approximation properties of deep neural networks. The work in this paper extends the CohereNet architecture to perform binary classification and extract unique features from raw ultrasound data that differentiate benign from malignant breast masses. This extended network classified breast masses as benign or malignant with 83% classification accuracy when tested with data that was not used during training. With the CohereNet architecture as the backbone of this extended network, we leverage coherence features during training and ultimately display network-modified coherence functions. Overall, our extended network architecture and training strategy are promising for a new class of deep learning methods that simultaneously extract features from raw ultrasound data, retain physical interpretations, and diagnose breast masses as benign or malignant.