Denoising Diffusion Medical Models

In this study, we introduce a generative model that can synthesize a large number of radiographical image/label pairs, and thus is asymptotically favorable to downstream activities such as segmentation in bio-medical image analysis. Denoising Diffusion Medical Model (DDMM), the proposed technique, can create realistic X-ray images and associated segmentations on a small number of annotated datasets as well as other massive unlabeled datasets with no supervision. Radiograph/segmentation pairs are generated jointly by the DDMM sampling process in probabilistic mode. As a result, a vanilla UNet that uses this data augmentation for segmentation task outperforms other similarly data-centric approaches.

Neural Radiance Projection

The proposed method, Neural Radiance Projection (NeRP), addresses the three most fundamental shortages of training such a convolutional neural network on X-ray image segmentation: dealing with missing/limited human-annotated datasets; ambiguity on the per-pixel label; and the imbalance across positive- and negative- classes distribution. By harnessing a generative adversarial network, we can synthesize a massive amount of physics-based X-ray images, so-called Variationally Reconstructed Radiographs (VRRs), alongside their segmentation from more accurate labeled 3D Computed Tomography data. As a result, VRRs present more faithfully than other projection methods in terms of photo-realistic metrics. Adding outputs from NeRP also surpasses the vanilla UNet models trained on the same pairs of X-ray images.