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The classic metaphyseal lesion (CML) is a distinct injury that is highlyspecific for infant abuse. It commonly occurs in the distal tibia. To aidradiologists detect these subtle fractures, we need to develop a model that canflag abnormal distal tibial radiographs (i.e. those with CMLs). Unfortunately,the development of such a model requires a large and diverse training database,which is often not available. To address this limitation, we propose a novelgenerative model for data augmentation. Unlike previous models that fail togenerate data that span the diverse radiographic appearance of the distaltibial CML, our proposed masked conditional diffusion model (MaC-DM) not onlygenerates realistic-appearing and wide-ranging synthetic images of the distaltibial radiographs with and without CMLs, it also generates their associatedsegmentation labels. To achieve these tasks, MaC-DM combines the weightedsegmentation masks of the tibias and the CML fracture sites as additionalconditions for classifier guidance. The augmented images from our modelimproved the performances of ResNet-34 in classifying normal radiographs andthose with CMLs. Further, the augmented images and their associatedsegmentation masks enhanced the performance of the U-Net in labeling areas ofthe CMLs on distal tibial radiographs.

Masked Conditional Diffusion Models for Image Analysis with Application to Radiographic Diagnosis of Infant Abuse