DeepTechnome: Mitigating Unknown Bias in Deep Learning Based Assessment of CT Images
This addresses bias mitigation in medical imaging for improved disease detection, though it is incremental as it builds on existing debiasing techniques.
The paper tackles the problem of unknown bias in deep learning models for medical imaging by debiasing during training without prior knowledge, using control regions and a DecorreLayer to suppress biased features. On a dataset of 952 lung CT scans with simulated biases, the method near-perfectly recovers classification performance compared to training with unbiased data.
Reliably detecting diseases using relevant biological information is crucial for real-world applicability of deep learning techniques in medical imaging. We debias deep learning models during training against unknown bias - without preprocessing/filtering the input beforehand or assuming specific knowledge about its distribution or precise nature in the dataset. We use control regions as surrogates that carry information regarding the bias, employ the classifier model to extract features, and suppress biased intermediate features with our custom, modular DecorreLayer. We evaluate our method on a dataset of 952 lung computed tomography scans by introducing simulated biases w.r.t. reconstruction kernel and noise level and propose including an adversarial test set in evaluations of bias reduction techniques. In a moderately sized model architecture, applying the proposed method to learn from data exhibiting a strong bias, it near-perfectly recovers the classification performance observed when training with corresponding unbiased data.