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An anatomic-site aware masked transformer is introduced, which effectively models the patient context by considering all lesions in a patient by considering all lesions in a patient, which can be variable in count, and their site of incidence.
This work presents CheXtriev, a graph-based, anatomy-aware framework for chest radiograph retrieval that leverages graph transformers to extract informative features from specific anatomical regions and captures spatial context and the interplay between anatomical location and findings.
The results suggest medical vision encoders carry a map of where organs usually lie, and little of the machinery for comparing structures within a particular patient, in addition to five architectural configurations and three medical foundation models, frozen and finetuned.
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