"Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT

• URL: http://arxiv.org/abs/2008.08525v1
• Date: Wed, 19 Aug 2020 16:05:58 GMT
• Title: "Name that manufacturer". Relating image acquisition bias with task complexity when training deep learning models: experiments on head CT
• Authors: Giorgio Pietro Biondetti, Romane Gauriau, Christopher P. Bridge, Charles Lu, Katherine P. Andriole
• Abstract summary: We analyze how the distribution of scanner manufacturers in a dataset can contribute to the overall bias of deep learning models. We demonstrate that CNNs can learn to distinguish the imaging scanner manufacturer and that this bias can substantially impact model performance.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As interest in applying machine learning techniques for medical images continues to grow at a rapid pace, models are starting to be developed and deployed for clinical applications. In the clinical AI model development lifecycle (described by Lu et al. [1]), a crucial phase for machine learning scientists and clinicians is the proper design and collection of the data cohort. The ability to recognize various forms of biases and distribution shifts in the dataset is critical at this step. While it remains difficult to account for all potential sources of bias, techniques can be developed to identify specific types of bias in order to mitigate their impact. In this work we analyze how the distribution of scanner manufacturers in a dataset can contribute to the overall bias of deep learning models. We evaluate convolutional neural networks (CNN) for both classification and segmentation tasks, specifically two state-of-the-art models: ResNet [2] for classification and U-Net [3] for segmentation. We demonstrate that CNNs can learn to distinguish the imaging scanner manufacturer and that this bias can substantially impact model performance for both classification and segmentation tasks. By creating an original synthesis dataset of brain data mimicking the presence of more or less subtle lesions we also show that this bias is related to the difficulty of the task. Recognition of such bias is critical to develop robust, generalizable models that will be crucial for clinical applications in real-world data distributions.

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