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

Luisa Gallée; Catharina Silvia Lisson; Christoph Gerhard Lisson; Daniela Drees; Felix Weig; Daniel Vogele; Meinrad Beer; Michael Götz

arXiv · 2508.00639

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

Luisa Gallée·Catharina Silvia Lisson·Christoph Gerhard Lisson·Daniela Drees·Felix Weig·Daniel Vogele·Meinrad Beer·Michael Götz

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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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BibTeX RIS

Luisa Gallée, Catharina Silvia Lisson, Christoph Gerhard Lisson, Daniela Drees, Felix Weig, Daniel Vogele, Meinrad Beer, Michael Götz. 2025-08-01. Minimum Data, Maximum Impact: 20 annotated samples for explainable lung nodule classification. https://arxiv.org/abs/2508.00639

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