## 2 Materials and Methods
### Parametric Images from Radiofrequency Signals  
Ultrasound is a ubiquitous medical imaging modality that provides cross-sectional information about the human body. Acoustic waves, insonified by a carrier frequency in the ultrasonic range, are reflected or scattered from the organ boundary or microstructure  
Figure 1: Sample B-mode images obtained from the radiofrequency (RF) signals of the open access series of breast ultrasonic data (OASBUD) dataset. (**a**) Benign lesions; (**b**) Malignant lesions. of the soft tissue, respectively. The received sound waves, known as RF signals, carry information about the scanned body along the axial direction. Several display modes, such as the A-mode, B-mode, and M-mode, represent information according to clinical applications. B-mode imaging is the most common imaging mode that takes envelopes of analytic signals generated from a set of received RF signals and provides anatomical and microstructural information about the scanning area, which is closely related to the echogenicity of the tissue. Although B-mode imaging visualizes the ultrasound echogenic properties inside the human body, it is challenging to directly analyze and diagnose pathological status from a B-mode image owing to low image quality and artifacts. Recently, CAD systems using CNN-based deep learning techniques have been applied to discriminate breast masses and have achieved good performance compared to experienced sonographers in BUS applications [17; 18; 19]. However, the size of the available training datasets and the low quality of the B-mode image are the main obstacles for CNN-based deep learning methods in real clinical ultrasound applications. Although the virtue of deep learning is the automatic feature abstraction of input images by itself, there needs to be a breakthrough for relieving the limitation of datasets and image quality in clinical BUS. The B-mode image generally consists of strong reflections from the organ boundary and relatively weak echo scattering inside the soft tissue. Because a B-mode image is generated from the envelopes of RF signals (the envelopes of the magnitude of analytic signals calculated from RF signals), strong reflections appear as edges or boundaries of tumors showing anatomical information, and weak echoes appear as textual patterns by the interference of scattered waves, which are related to the microstructure of soft tissue in a B-mode image. This is a significant feature of breast US images for differentiating tumors or masses. However, the issue is that the size of the training datasets and ultrasound image quality is not sufficient for deep learning networks to extract meaningful features automatically from given B-mode images alone. Therefore, in this study, we proposed the use of additional parametric images that represent the distinct properties of soft tissue to diagnose the pathological status from ultrasound RF signals. Because a received RF signal contains rich information about the scanned region along the propagating path, including both the signal amplitude and instantaneous phase information, it can calculate or estimate various useful images (usually called feature maps or parametric images in the research field of medical ultrasound) that carry different tissue properties. If these additional parametric images emphasize or well-represent meaningful properties of soft tissue, such as boundaries, textual patterns, or QUS parameters, they could provide additional information to the deep learning network as extra inputs in addition to traditional B-mode images. In this study, entropy and phase images were utilized to represent visual properties in the time domain and attenuation images to show frequency-dependent characteristics, which is a significant QUS parameter in the spectral domain. The entropy image shows the properties of the textual pattern from the microstructure in a small region, whereas the phase image shows the morphological information, such as the edges and boundaries of a B-mode image, representing anatomical structures.