Mammographic image metadata learning for model pretraining and explainable predictions

Lester Litchfield; Melissa L. Hill; Nabeel Khan; Ralph Highnam

doi:10.1117/12.2626199

13 July 2022 Mammographic image metadata learning for model pretraining and explainable predictions

Lester Litchfield, Melissa L. Hill, Nabeel Khan, Ralph Highnam

Proceedings Volume 12286, 16th International Workshop on Breast Imaging (IWBI2022); 1228616 (2022) https://doi.org/10.1117/12.2626199
Event: Sixteenth International Workshop on Breast Imaging, 2022, Leuven, Belgium

Abstract

Purpose: To introduce a novel technique for pretraining deep neural networks on mammographic images, where the network learns to predict multiple metadata attributes and simultaneously to match images from the same patient and study. Further to demonstrate how this network can be used to produce explainable predictions. Methods: We trained a neural network on a dataset of 85,558 raw mammographic images and seven types of metadata, using a combination of supervised and self-supervised learning techniques. We evaluated the performance of our model on a dataset of 4,678 raw mammographic images using classification accuracy and correlation. We also designed an ablation study to demonstrate how the model can produce explainable predictions. Results: The model learned to predict all but one of the seven metadata fields with classification accuracy ranging from 78-99% on the validation dataset. The model was able to predict which images were from the same patient with over 93% accuracy on a balanced dataset. Using a simple X-ray system classifier built on top of the first model, representations learned on the initial X-ray system classification task showed by far the largest effect size on ablation, illustrating a method for producing explainable predictions. Conclusions: It is possible to train a neural network to predict several kinds of mammogram metadata simultaneously. The representations learned by the model for these tasks can be summed to produce an image representation that captures features unique to a patient and study. With such a model, ablation offers a promising method to enhance the explainability of deep learning predictions.

Citation Download Citation

Lester Litchfield, Melissa L. Hill, Nabeel Khan, and Ralph Highnam "Mammographic image metadata learning for model pretraining and explainable predictions", Proc. SPIE 12286, 16th International Workshop on Breast Imaging (IWBI2022), 1228616 (13 July 2022); https://doi.org/10.1117/12.2626199

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KEYWORDS

Data modeling

X-rays

Breast

Mammography

Digital breast tomosynthesis

X-ray imaging

Breast cancer

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