## 1 Introduction
), publicly available on the Internet and with thousands of images of different pathologic conditions, has enabled the research and development of machine-learning techniques to improve the detection of several conditions, providing us with powerful Internet-based multi-source data, with pneumothorax being one of the labels present in many of those datasets. But there some challenges with the usage of these datasets for pneumothorax detection [31]:  
First, many datasets use NLP techniques for extracting labels out of radiology reports (instead of using expert labelling from radiologists directly), which has led to the inconsistent assignment of labels due to NLP limitations and implicit knowledge in the reports. Some of the datasets, such as Indiana, MIMIC-CXR, PadChest, and, recently, CheXpert Plus [32] (an extension of the original CheXpert dataset), also release the radiology reports along with the images, in an attempt to allow research on the information extraction from the reports. Second, some of the datasets suffer confounding biases due to treated pneumothoraces with the presence of chest tubes, labelled as pneumothorax-positive and sometimes even without visible pneumothorax in the chest X-ray images, a fact that limits the power of generalization if not mitigated properly [33]. Third, many of the datasets do not include the size or localization of the pneumothorax (small vs. large, and left vs. right lung), and few of them include information that would allow object detection or segmentation, information that would be important for evaluating the performance of difficult cases (small pneumothoraces). There have been recent attempts to overcome those limitations: ref. [34] showed that expert-labeled models outperformed NLP-labeled models and released enhanced labels for four diseases in a subset of the NIH dataset (pneumothorax amongst them). Ref. [35] released new manually curated labels for all pathologies for a subset of the NIH dataset. Ref. [36] trained a model to annotate chest tubes and applied it to the NIH and PadChest datasets, publicly releasing their annotations [37]. Ref. [38] performed a review for the entire NIH using the expert radiologists' dataset, regenerating the entire pneumothorax label, and showing that 2231/5302 were false positive labels (no actual pneumothorax), and 2067/106,818 were false negative (labeled as no-pneumothorax but actually pathological), releasing their new labels for public use. Ref. [39] trained Mask R-CNN models for pneumothorax detection and released the segmentation masks they used for the training. All this evidence shows that, despite pneumothorax being an important condition that needs to be ruled out in the emergency department, the use of machine learning for detection and triaging has some difficulties that need to be addressed in order to achieve clinical implementation. In this paper, we present a solution based on multi-source data publicly available on the Internet. Our main insight is that, by unifying and harmonizing all available datasets, we can obtain a more powerful set of data that has the potential to improve the results of deep-learning methods. In this sense, we create a multi-source dataset that demonstrates its advantages over individual datasets when training CNN-based models. Our proposed model has the potential to be used in a general emergency department in any hospital since the model includes datasets from different hospitals and countries. The generalization capabilities of this new model are demonstrated in the resulting experiments in this paper.