Chest X-rays are a common diagnostic tool in the medical field, allowing healthcare professionals to detect various pathologies within the chest area. However, merely identifying the presence of a condition like pneumonia is not always sufficient; localization of abnormalities is crucial for accurate diagnosis and treatment planning. “The main problem is there’s very little data available for those localizations,” Philip begins. “There are very few datasets, and they need to be hand-labelled, which is challenging and time-consuming. You typically approach that by using weakly supervised object detection methods, which means you use classification labels to train an object detector. However, this isn’t optimal because it’s hard to localize based on classification labels alone. It doesn’t work well without bounding boxes, but bounding boxes for pathologies are expensive. You need a well-trained radiologist to spot the pathologies correctly.” Why do we need to improve localization in chest X-rays? The answer is twofold. First, from a research perspective, chest X-ray data is abundant and readily accessible, making it an attractive choice for developing and testing algorithms. Second, in a clinical context, chest X-rays are cost-effective and widely available in most healthcare facilities. A rapid detection algorithm that provides preliminary insights or assists radiologists and other medical professionals could be invaluable, particularly in emergencies. 16 DAILY MICCAI Monday Poster Presentation Anatomy-Driven Pathology Detection on Chest X-rays Philip Müller is a PhD student at Daniel Rueckert’s Lab for AI in Medicine at TU Munich. His work proposes a pathology detection localization method in chest X-rays. He speaks to us ahead of his poster this afternoon.
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