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Diffusion inpainting generates healthy mammograms to boost cancer detection accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel counterfactual data augmentation technique using diffusion inpainting to improve mammography classification for computer-aided diagnosis (CAD) systems. This method generates realistic healthy mammograms by removing lesions from anomalous images, thereby enhancing the training data distribution. The generated healthy counterfactuals maintain patient-specific anatomy and were found to be realistic by radiologists. Experiments on the VinDr-Mammo dataset demonstrated improved sensitivity across various classifier architectures, including ConvNeXt, ViT, Mammo-CLIP, and FPN-MIL, contributing to more reliable CAD systems. AI

IMPACT Enhances diagnostic accuracy in medical imaging by improving AI model training data.

RANK_REASON Academic paper detailing a new method for data augmentation in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Diffusion inpainting generates healthy mammograms to boost cancer detection accuracy

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Academic paper detailing a new method for data augmentation in medical imaging. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · In\^es Cruchinho Garcia, Mariana Mour\~ao, Francisco Maria Calisto, Carlos Santiago, Jacinto Nascimento ·

    Healthy Counterfactual Generation via Diffusion Inpainting for Mammography Classification

    arXiv:2610.12147v1 Announce Type: new Abstract: False negatives remain a critical limitation of computer-aided diagnosis (CAD) systems for breast cancer screening due to delayed detection and treatment. To address this issue, we propose a counterfactual data augmentation strategy…

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