Alleviating Data Imbalance Issue with Perturbed Input During Inference

Intelligent diagnosis is often biased toward common diseases due to data imbalance between common and rare diseases. Such bias may still exist even after applying re-balancing strategies during model training. To further alleviate the bias, we propose a novel method which works not in the training but in the inference phase. For any test input data, based on the difference between the temperature-tuned classifier output and a target probability distribution derived from the inverse frequency of different diseases, the input data can be slightly perturbed in a way similar to adversarial learning. The classifier prediction for the perturbed input would become less biased toward common diseases compared to that for the original one. The proposed inference-phase method can be naturally combined with any training-phase re-balancing strategies. Extensive evaluations on three different medical image classification tasks and three classifier backbones support that our method consistently improves the performance of the classifier which even has been trained by any re-balancing strategy. The performance improvement is substantial particularly on minority classes, confirming the effectiveness of the proposed method in alleviating the classifier bias toward dominant classes.