Localization with Limited Annotation for Chest X-rays
This addresses the high cost of annotation for medical imaging tasks, offering a weakly supervised approach that reduces the need for extensive labeled data.
The paper tackles the problem of object localization in medical imaging with minimal bounding box annotations, achieving state-of-the-art performance on the ChestX-ray14 dataset by using a novel loss function and architecture that incorporates CRF layers and anti-aliasing filters.
Localization of an object within an image is a common task in medical imaging. Learning to localize or detect objects typically requires the collection of data which has been labelled with bounding boxes or similar annotations, which can be very time consuming and expensive. A technique which could perform such learning with much less annotation would, therefore, be quite valuable. We present such a technique for localization with limited annotation, in which the number of images with bounding boxes can be a small fraction of the total dataset (e.g. less than 1%); all other images only possess a whole image label and no bounding box. We propose a novel loss function for tackling this problem; the loss is a continuous relaxation of a well-defined discrete formulation of weakly supervised learning and is numerically well-posed. Furthermore, we propose a new architecture which accounts for both patch dependence and shift-invariance, through the inclusion of CRF layers and anti-aliasing filters, respectively. We apply our technique to the localization of thoracic diseases in chest X-ray images and demonstrate state-of-the-art localization performance on the ChestX-ray14 dataset.