We describe the case of a female patient, 52 years old, with dizziness and left motor incoordination for 2 weeks. Brain MRI magnetic resonance imaging) revealed a hyperintense lesion on T2-weighted images, without restricted diffusion, in the left middle cerebellar peduncle. Spectroscopy demonstrated peak of lipids and perfusion did not show any elevation in relative cerebral blood volume (rCBV). The patient underwent an open biopsy and resection, and the diagnosis of diffuse large B-cell lymphoma (DLBCL) was established. The patient received intravenous dexamethasone with symptoms remission, followed by four cycles of methotrexate plus cytarabine. After 3 months, the patient returned with decreased consciences level and a new MRI revealed a right superior frontal gyrus lesion with features suggesting a lymphomatous lesion. The patient died five days after her relapse.
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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.
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