Breast Ultrasound Tumor Generation via Mask Generator and Text-Guided Network:A Clinically Controllable Framework with Downstream Evaluation
Journal:
arXiv
Published Date:
Jul 10, 2025
Abstract
The development of robust deep learning models for breast ultrasound (BUS)
image analysis is significantly constrained by the scarcity of expert-annotated
data. To address this limitation, we propose a clinically controllable
generative framework for synthesizing BUS images. This framework integrates
clinical descriptions with structural masks to generate tumors, enabling
fine-grained control over tumor characteristics such as morphology,
echogencity, and shape. Furthermore, we design a semantic-curvature mask
generator, which synthesizes structurally diverse tumor masks guided by
clinical priors. During inference, synthetic tumor masks serve as input to the
generative framework, producing highly personalized synthetic BUS images with
tumors that reflect real-world morphological diversity. Quantitative
evaluations on six public BUS datasets demonstrate the significant clinical
utility of our synthetic images, showing their effectiveness in enhancing
downstream breast cancer diagnosis tasks. Furthermore, visual Turing tests
conducted by experienced sonographers confirm the realism of the generated
images, indicating the framework's potential to support broader clinical
applications.
