## 4 Dataset Acquisition  
One of the main challenges in developing CAD systems is acquiring extensive and heterogeneous datasets that can provide effective training for the model. For this study, we assembled a dataset comprising 16,710 exams. Among these, 6667 (39.9%) are classified as normal and 10,043 (60.1%) as abnormal, indicating a predominance of exams performed due to specific findings, leading to a certain imbalance in the data. The primary objective of this work is to use pododactyl radiographs and medical reports to automatically generate medical reports using generative models. Thus, despite having class labels, we will use the dataset without class separation for this study; class differentiation will only be considered in the analysis of the results, as our main goal is to generate reports for these exams regardless of their clinical classification. Figure 1 illustrates the data acquisition process. Although this imbalance in the dataset, with a greater number of abnormal cases, is a common concern in many machine learning applications, in this specific context of the automated generation of polydactyl medical reports, we understand that it does not represent a significant problem. In fact, we recognize that the greater representation of the abnormal class is beneficial, as these samples exhibit more variability in the features that classify them as abnormal. This diversity is important for training models that can accurately capture and report a wide range of abnormal conditions. In contrast, normal cases tend to be more homogeneous, and their smaller proportion does not significantly affect the model's ability to learn to identify and describe abnormal patterns. Therefore, the imbalance in the dataset can contribute to the robustness of the model in identifying anomalies, aligning with our goal of creating an effective system for generating automated medical reports. This dataset was collected from hospitals and clinics in all regions of Brazil (North, Northeast, Central-West, Southeast, and South), providing heterogeneity and a wide variety of resolutions, angles, genders, and age ranges, totaling 16,710 exam samples with images and anonymous reports. The exams were obtained in DICOM format and converted to \".png\" format for subsequent processing. The images have the Photometric attribute with the Monochrome2 parameter, where higher intensity pixels represent light colors and lower intensity pixels represent dark colors. Figure 3 shows examples of frontal and lateral view images from the same exam. Each view provides specific information about the patient's condition. For exam labeling, we used the Radiologist Report Interpretation (RIR) methodology [36; 37], where specialists analyze the medical reports and classify them according to their content. This methodology was used for all normal class exams and part of the abnormal exams. The dataset used in this study includes radiographs of Polydactyls and their corresponding medical reports produced by specialists. The reports are divided into sections such as \"FINDINGS\" and \"IMPRESSIONS\", highlighting important characteristics of the images and summarizing their clinical interpretations [23]. We focused on these sections for analysis. Figure 4 shows a Polydactyly radiograph with its medical report in Portuguese and its translation into English. It also displays four radiographs and their respective reports in both languages, providing a comprehensive view of the data. To create a model for the automatic generation of medical reports, it is crucial to understand the associated dataset. In Figure 5, we see the distribution of medical reports, which helps us determine the maximum length of a report that our model should generate in terms of words. After analyzing the data, we found 2256 unique words in the dataset, which forms the available vocabulary for the model.