## 1 Introduction
In previous studies, [PERSON] et al. [25] used numerical mediums composed of discs for training and test data. [PERSON] et al. [26] used numerical mediums composed of ellipses as training data and tested them on both numerical and physical phantoms of the same geometry. [PERSON] et al. [24] used numerical breast phantoms for training data and tested on similar phantoms. Such evaluation systems may not accurately evaluate the generalization performance to out-of-distribution data. From this viewpoint, [PERSON] et al. [28] have examined how the performance of sound speed reconstruction by DNNs depends on the data domain. They showed that a DNN trained on breast phantom data performed poorly on elliptical data; similarly, a DNN trained on elliptical data performed poorly on breast phantom data. This result suggests that when DNNs are trained on datasets with limited patterns, such as breast phantom data and elliptical data, they do not perform well on data outside their domain. As a countermeasure to this low generalization performance, the mixing several types of datasets is proposed. However, this approach requires individual consideration for each application to determine the type and number of datasets to be prepared. Typical breast datasets are produced by artificially assigning sound speeds to image data such as MRI images [20; 24; 28; 29]. However, they do not necessarily correspond to the true sound speed of the tissue. In addition, since DNNs are trained to fit the domain of the training data, there is a risk wherein the output is predicted to have similar features to the artificial training data. Therefore, if estimation results are subjectively valid, there is a risk that they may not have physically meaningful sound speed. Another issue is data imbalance [30]. The main purpose of breast examination is to detect abnormal structures such as tumours, rather than normal tissue structures. However, data on such abnormal structures are rare compared to normal structures, and it is difficult to collect enough data. Abnormal structures that appear less frequently are less important in training and are therefore expected to be inferior to the imaging ability of normal structures that appear more frequently [31]. For these reasons, there is a need to develop a single training dataset that contains sufficiently diverse patterns and achieves high generalization performance for unknown patterns, such as tumours. For this need, we propose a method to produce training data based on natural images [32; 33; 34; 35]. There are many publicly available datasets of natural images, and their structures are far more diverse than the ones produced from breast phantoms or ellipse combinations. By utilizing these diverse structures in the training dataset's sound speed distribution, it may be possible to produce a single training dataset that leads to high generalization performance. From the above, this study aims to develop a dataset based on natural images for a DNN that solves an inverse problem of sound speed reconstruction from observed signals. In addition, we aim to evaluate the generalization performance of the DNN trained on the natural image dataset in comparison to the case of the breast phantom dataset.