Fig. 2: Prototype representation space of SongCi & results of post-mortem image generation.
From: Large-vocabulary forensic pathological analyses via prototypical cross-modal contrastive learning
a The prototype representation space is visualized using a 2D UMAP, where 933 dots represent the prototypes, with each dot colored according to the proportion of tissue types it represents. Source data are provided as a Source Data file. b, c Prototype-conditioned patch-level generation results. Sub-figure b shows the results for inter-tissue-specific prototypes, including autolysis, inflammation, fibrosis, and hemorrhage. Sub-figure (c) displays intra-tissue-specific prototypes, such as myocardial hypertrophy, cerebral edema, muscular tissue, pneumorrhagia, and hepatic steatosis. d, e The conditional diffusion models are exhibited. Sub-figure (d) illustrates the prototype-based conditional diffusion model, and sub-figure (e) shows the instance-based model. f The results of instance-based patch-level generation are presented, featuring representative instances like renal tubules with hemorrhage, normal renal tubules, liver fat particles (undissolved), and splenic trabeculae.
