Among the various methods explored to improve localization in chest Xrays, some have shown promise, while others have fallen short of expectations. “Methods that typically work well on natural images don’t work at all on chest X-rays because they’re based on unsupervised bounding box proposals, which often use some form of edge detection not well suited to box proposals for pathologies,” Philip tells us. “Other methods that work on chest X-rays are CAM-based models, so class activation mapping, where you train a classifier and then look at which patches would be classified by that. They work to some degree but still are not working well.” One approach Philip discovered holds promise is using anatomic regions as bounding boxes. Anatomic regions are specific areas within the chest, such as regions in the lungs or the cardiac silhouette. It is a simpler and less costly task, making it feasible for medical students to contribute. The speed at which object detection models like a Faster R-CNN can be trained with just a few hundred samples makes annotating anatomic regions even more appealing. By combining classification labels extracted automatically from radiology reports with some annotated anatomic regions, researchers can detect the anatomic regions for a vast dataset easily and cheaply. “I tried two different things with this work,” Philip reveals. “The first one is using image-level classification labels and those anatomic regions, which already showed promising results and improved on those weakly supervised 17 DAILY MICCAI Monday Philip Müller
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