Speaker
Description
Detecting and subtyping mitotic figures in histopathology is central to tumor grading as well as patient survival prognosis, yet training data for rare phases and atypical mitoses remains scarce and costly to annotate, thus, generating synthetic data for these cases is desirable. Generative models have been introduced in the past that copy and paste mitotic chromatin structure from one context to another, yet the augmentation capability of such models are limited only to the context and not the chromatin structure itself.
We introduce Mito-Syn, a two-stage pipeline that decouples chromatin structure (location and shape) from tissue texture, grounding each stage in a different form of domain knowledge. First, a conditional generative shape model, conditioned on mitotic phase and cell boundary geometry, trained on thousands of mitotic cells from different mitotic subphases, synthesizes a binary chromatin mask. Second, a conditional inpainting model paints the tissue texture inside the cell boundary, conditioned on mitotic phase and the chromatin mask generated by stage 1. For the second stage, we also introduce an alternative language-grounded inpainting technique, where the inpainting is conditioned on captions made by a pathology domain expert, focusing on the distinct characteristics of the specific mitotic phases.
We showcase the augmentation value of our method on publicly available mitosis subtyping benchmark datasets. Our pipeline yields controllable, biologically consistent synthetic mitotic figures across the full phase spectrum. By explicitly disentangling morphology from appearance and incorporating expert language descriptions as a conditioning signal, our approach offers an interpretable and controllable alternative to end-to-end image generation for medical image synthesis.