Srikar Yellapragada

Histopathology image generation has stayed at the patch scale, producing tiles of at most 1024×1024 pixels, while clinical text reports and slide-level diagnostic labels act on the whole slide. BigCell trains a flow-matching transformer at 2048×2048 pixels on over 120,000 public whole-slide images and uses an inference-time sliding-window pipeline to generate coherent images up to 32,768×32,768 pixels, outperforming prior tile generators on tile-level and slide-level fidelity metrics. An adaptive scheduling strategy speeds generation by over three times with minimal quality degradation, and synthetic samples improve slide-level few-shot classification by up to 18%.

Histopathology image generation has stayed at the patch scale, producing tiles of at most 1024×1024 pixels, while clinical text reports and slide-level diagnostic labels act on the whole slide. BigCell trains a flow-matching transformer at 2048×2048 pixels on over 120,000 public whole-slide images and uses an inference-time sliding-window pipeline to generate coherent images up to 32,768×32,768 pixels, outperforming prior tile generators on tile-level and slide-level fidelity metrics. An adaptive scheduling strategy speeds generation by over three times with minimal quality degradation, and synthetic samples improve slide-level few-shot classification by up to 18%.