Reconstruction of Patient-Specific Confounders in AI-based Radiologic
  Image Interpretation using Generative Pretraining
Tianyu Han Laura Žigutytė Luisa Huck Marc Huppertz Robert Siepmann Yossi Gandelsman Christian Blüthgen Firas Khader Christiane Kuhl Sven Nebelung Jakob Kather Daniel Truhn
Detecting misleading patterns in automated diagnostic assistance systems,
such as those powered by Artificial Intelligence, is critical to ensuring their
reliability, particularly in healthcare. Current techniques for evaluating deep
learning models cannot visualize confounding factors at a diagnostic level.
Here, we propose a self-conditioned diffusion model termed DiffChest and train
it on a dataset of 515,704 chest radiographs from 194,956 patients from
multiple healthcare centers in the United States and Europe. DiffChest explains
classifications on a patient-specific level and visualizes the confounding
factors that may mislead the model. We found high inter-reader agreement when
evaluating DiffChest's capability to identify treatment-related confounders,
with Fleiss' Kappa values of 0.8 or higher across most imaging findings.
Confounders were accurately captured with 11.1% to 100% prevalence rates.
Furthermore, our pretraining process optimized the model to capture the most
relevant information from the input radiographs. DiffChest achieved excellent
diagnostic accuracy when diagnosing 11 chest conditions, such as pleural
effusion and cardiac insufficiency, and at least sufficient diagnostic accuracy
for the remaining conditions. Our findings highlight the potential of
pretraining based on diffusion models in medical image classification,
specifically in providing insights into confounding factors and model
robustness.