MAMBO: High-Resolution Generative Approach for Mammography Images
Journal:
arXiv
Published Date:
Jun 10, 2025
Abstract
Mammography is the gold standard for the detection and diagnosis of breast
cancer. This procedure can be significantly enhanced with Artificial
Intelligence (AI)-based software, which assists radiologists in identifying
abnormalities. However, training AI systems requires large and diverse
datasets, which are often difficult to obtain due to privacy and ethical
constraints. To address this issue, the paper introduces MAMmography ensemBle
mOdel (MAMBO), a novel patch-based diffusion approach designed to generate
full-resolution mammograms. Diffusion models have shown breakthrough results in
realistic image generation, yet few studies have focused on mammograms, and
none have successfully generated high-resolution outputs required to capture
fine-grained features of small lesions. To achieve this, MAMBO integrates
separate diffusion models to capture both local and global (image-level)
contexts. The contextual information is then fed into the final patch-based
model, significantly aiding the noise removal process. This thoughtful design
enables MAMBO to generate highly realistic mammograms of up to 3840x3840
pixels. Importantly, this approach can be used to enhance the training of
classification models and extended to anomaly detection. Experiments, both
numerical and radiologist validation, assess MAMBO's capabilities in image
generation, super-resolution, and anomaly detection, highlighting its potential
to enhance mammography analysis for more accurate diagnoses and earlier lesion
detection.
