Sebastian Gündel
Recognition of Similarities and Abnormalities in Chest Radiographs
In a medical sense, radiography is an imaging technique to produce a plain image of the human body. Radiography of the chest is the most commonly used medical image acquisition method. In contrast to other imaging techniques, radiography benefits from fast examination procedures and low radiation doses. Moreover, a chest radiograph delivers detailed findings of diseases, pathologies, and abnormal structures in the thoracic and abdominal region. For example, fluid retention in the lung, an enlarged heart, or lung nodules specify only a fraction of the large variety of abnormalities visible in chest radiographs. With the breakout of the COVID-19 disease, an additional major use case was integrated into the clinical workflow for chest radiography assessment. The high amount of produced radiographic images require a time-consuming reading process by clinical professionals. The daily workload and the connected liability often hinder the assurance of a consistent image reading accuracy. In other words, human-like behavior such as fatigue, concentration lack, and time pressure reduces performance. To maintain a constant reading output, so-called computer-aided detection (CAD) systems are incorporated into the clinical workflow to compensate for the pitfalls of the human. Moreover, applying these systems not only simplifies the reading process for the experts but also reduces the reading time. In the beginning, the integrated systems were based on conventional and rule-based methods, e.g., image gradient filters. However, with the start of the deep learning (DL) era, including the availability of a huge number of training data, the performance of CAD systems rapidly increased. To leverage these systems, advanced systems are developed to increase the generalizability of such networks. Especially lung nodules are challenging to read as it appears as a tiny fraction in the image. Due to the resulting low system performance, augmentation methods are designed to specifically focus on improving nodule recognition. For example, nodules of computed tomography (CT) images are leveraged to expand the nodule collection for training in the radiography domain. The proposed approaches based on DL require a huge amount of training data. The demand for big chest radiography collections results in the ever-growing number of datasets publicly available. In practice, strict anonymization procedures are applied prior to the release of the data. For example, sensitive data such as patient names are removed. However, these anonymization methods may facilitate that the patient can be re-identified from the medical image content. For example, sensitive content can be linked between images by a system which identifies images from the same patient. This scenario highlights lacks in the current anonymization procedures as well as possible risks in data privacy and security.