Mixing-AdaSIN: Constructing a de-biased dataset using Adaptive Structural Instance Normalization and texture Mixing

• URL: http://arxiv.org/abs/2103.14255v1
• Date: Fri, 26 Mar 2021 04:40:14 GMT
• Title: Mixing-AdaSIN: Constructing a de-biased dataset using Adaptive Structural Instance Normalization and texture Mixing
• Authors: Myeongkyun Kang, Philip Chikontwe, Miguel Luna, Kyung Soo Hong, June Hong Ahn, Sang Hyun Park
• Abstract summary: We propose Mixing-AdaSIN; a bias mitigation method that uses a generative model to generate de-biased images. To demonstrate the efficacy of our method, we construct a biased COVID-19 vs. bacterial pneumonia dataset.
• Score: 6.976822832216875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Following the pandemic outbreak, several works have proposed to diagnose COVID-19 with deep learning in computed tomography (CT); reporting performance on-par with experts. However, models trained/tested on the same in-distribution data may rely on the inherent data biases for successful prediction, failing to generalize on out-of-distribution samples or CT with different scanning protocols. Early attempts have partly addressed bias-mitigation and generalization through augmentation or re-sampling, but are still limited by collection costs and the difficulty of quantifying bias in medical images. In this work, we propose Mixing-AdaSIN; a bias mitigation method that uses a generative model to generate de-biased images by mixing texture information between different labeled CT scans with semantically similar features. Here, we use Adaptive Structural Instance Normalization (AdaSIN) to enhance de-biasing generation quality and guarantee structural consistency. Following, a classifier trained with the generated images learns to correctly predict the label without bias and generalizes better. To demonstrate the efficacy of our method, we construct a biased COVID-19 vs. bacterial pneumonia dataset based on CT protocols and compare with existing state-of-the-art de-biasing methods. Our experiments show that classifiers trained with de-biased generated images report improved in-distribution performance and generalization on an external COVID-19 dataset.

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