  Visual attribution in medical imaging seeks to make evident the
diagnostically-relevant components of a medical image, in contrast to the more
common detection of diseased tissue deployed in standard machine vision
pipelines (which are less straightforwardly interpretable/explainable to
clinicians). We here present a novel generative visual attribution technique,
one that leverages latent diffusion models in combination with domain-specific
large language models, in order to generate normal counterparts of abnormal
images. The discrepancy between the two hence gives rise to a mapping
indicating the diagnostically-relevant image components. To achieve this, we
deploy image priors in conjunction with appropriate conditioning mechanisms in
order to control the image generative process, including natural language text
prompts acquired from medical science and applied radiology. We perform
experiments and quantitatively evaluate our results on the COVID-19 Radiography
Database containing labelled chest X-rays with differing pathologies via the
Frechet Inception Distance (FID), Structural Similarity (SSIM) and Multi Scale
Structural Similarity Metric (MS-SSIM) metrics obtained between real and
generated images. The resulting system also exhibits a range of latent
capabilities including zero-shot localized disease induction, which are
evaluated with real examples from the cheXpert dataset.
