A novel approach to remove foreign objects from chest X-ray images
This work addresses a domain-specific issue for medical imaging by enhancing computer-aided diagnostic predictions in chest radiographs, though it appears incremental as it builds on existing methods for object removal and inpainting.
The paper tackled the problem of removing foreign objects from chest X-ray images to improve diagnostic quality, achieving state-of-the-art accuracy by using a deep learning approach that combines object detection, segmentation, and inpainting on the cheXphoto dataset.
We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset. Foreign objects which can significantly affect the quality of a computer-aided diagnostic prediction are captured under various settings. In this paper, we used multi-method to tackle both removal and inpainting chest radiographs. Firstly, an object detection model is trained to separate the foreign objects from the given image. Subsequently, the binary mask of each object is extracted utilizing a segmentation model. Each pair of the binary mask and the extracted object are then used for inpainting purposes. Finally, the in-painted regions are now merged back to the original image, resulting in a clean and non-foreign-object-existing output. To conclude, we achieved state-of-the-art accuracy. The experimental results showed a new approach to the possible applications of this method for chest X-ray images detection.