To advance precision medicine in pathology, artificial intelligence (AI)-driven foundation models must generalize across diverse datasets, tissues, and clinical tasks. However, their comparative performance and generalizability in computational pathology remain incompletely chara…
Controllable generative models of 3D medical images can synthesize volumes with specified clinical attributes, but this demands samples that are simultaneously high-fidelity, natively 3D, and faithful to the requested conditioning. We present CONFLUX, a latent diffusion model for…
Diffusion language models, which generate text by denoising a token canvas bidirectionally instead of emitting tokens left to right, have become competitive with autoregressive (AR) generation. Medical foundation models, however, remain almost entirely autoregressive. We adapt a…