Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification

• URL: http://arxiv.org/abs/2508.00639v1
• Date: Fri, 01 Aug 2025 13:54:34 GMT
• Title: Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification
• Authors: Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz,
• Abstract summary: Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making.<n>The adoption of such models is limited by the scarcity of large-scale medical image datasets annotated with these attributes.<n>This work highlights the potential of synthetic data to overcome dataset limitations, enhancing the applicability of explainable models in medical image analysis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Classification models that provide human-interpretable explanations enhance clinicians' trust and usability in medical image diagnosis. One research focus is the integration and prediction of pathology-related visual attributes used by radiologists alongside the diagnosis, aligning AI decision-making with clinical reasoning. Radiologists use attributes like shape and texture as established diagnostic criteria and mirroring these in AI decision-making both enhances transparency and enables explicit validation of model outputs. However, the adoption of such models is limited by the scarcity of large-scale medical image datasets annotated with these attributes. To address this challenge, we propose synthesizing attribute-annotated data using a generative model. We enhance the Diffusion Model with attribute conditioning and train it using only 20 attribute-labeled lung nodule samples from the LIDC-IDRI dataset. Incorporating its generated images into the training of an explainable model boosts performance, increasing attribute prediction accuracy by 13.4% and target prediction accuracy by 1.8% compared to training with only the small real attribute-annotated dataset. This work highlights the potential of synthetic data to overcome dataset limitations, enhancing the applicability of explainable models in medical image analysis.

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