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A case-based interpretable deep learning model for classification of mass lesions in digital mammography

Abstract

Interpretability in machine learning models is important in high-stakes decisions such as whether to order a biopsy based on a mammographic exam. Mammography poses important challenges that are not present in other computer vision tasks: datasets are small, confounding information is present and it can be difficult even for a radiologist to decide between watchful waiting and biopsy based on a mammogram alone. In this work we present a framework for interpretable machine learning-based mammography. In addition to predicting whether a lesion is malignant or benign, our work aims to follow the reasoning processes of radiologists in detecting clinically relevant semantic features of each image, such as the characteristics of the mass margins. The framework includes a novel interpretable neural network algorithm that uses case-based reasoning for mammography. Our algorithm can incorporate a combination of data with whole image labelling and data with pixel-wise annotations, leading to better accuracy and interpretability even with a small number of images. Our interpretable models are able to highlight the classification-relevant parts of the image, whereas other methods highlight healthy tissue and confounding information. Our models are decision aids—rather than decision makers—and aim for better overall human–machine collaboration. We do not observe a loss in mass margin classification accuracy over a black box neural network trained on the same data.

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Fig. 1: An overview of IAIA-BL compared to other approaches.
Fig. 2: Fine-annotation regularization on model attention that penalizes the model for using confounding information.
Fig. 3: ROC curves of IAIA-BL compared with baselines.
Fig. 4: Case-based explanations generated by IAIA-BL.

Data availability

The imaging data are not publicly available because they contain confidential information that may compromise patient privacy as well as the ethical or regulatory policies of our institution. Data will be made available on reasonable request, for non-commercial research purposes, to those who contact J.L. (joseph.lo@duke.edu). Data usage agreements may be required. Source Data are provided with this paper.

Code availability

Code is available on GitHub at https://github.com/alinajadebarnett/iaiabl. Two licenses are offered: an MIT license for non-commercial use and a custom license. The doi for the initial code release is https://doi.org/10.5281/zenodo.5565592.

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Acknowledgements

We would like to acknowledge breast radiologists M. Taylor-Cho, L. Grimm, C. Kim and S. Yoon, who annotated the dataset used in this paper. This study was supported in part by NIH/NCI U01-CA214183 and U2C-CA233254 (J.L.). This study was supported in part by MIT Lincoln Laboratory (C.R.), Duke TRIPODS CCF-1934964 (C.R.) and the Duke Incubation Fund (A.J.B.).

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Contributions

A.J.B., F.S., D.T., C.C., J.L. and C.R. conceived the idea and developed the model. D.T., A.J.B. and C.C. wrote and reviewed the code. Y.R., A.J.B., F.S. and J.L. performed data collection, and Y.R., D.T. and A.J.B. preprocessed it.

Corresponding author

Correspondence to Alina Jade Barnett.

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The authors declare no competing interests.

Additional information

Peer review information Nature Machine Intelligence thanks Fredrik Strand and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 An automatically generated explanation of mass margin classification for a circumscribed lesion.

This circumscribed lesion is correctly identified as circumscribed. The first two most activated prototypes are drawn from the same image, but are associated with different regions of that image.

Extended Data Fig. 2 An automatically generated explanation of mass margin classification for an indistinct lesion.

This indistinct lesion is correctly identified as indistinct. The indistinct portion of the lesion margin (right side) activates the indistinct prototype and the circumscribed portion of the lesion margin (left side) activates the circumscribed prototypes.

Extended Data Fig. 3 An automatically generated explanation of mass margin classification for a spiculated lesion.

This spiculated lesion is correctly identified as spiculated.

Extended Data Fig. 4 An automatically generated explanation of mass margin classification for an incorrectly classified lesion.

This spiculated lesion is incorrectly identified as circumscribed. The explanation highlights only the circumscribed portion of the mass margin (top), but does not detect the spiculated portion (bottom).

Extended Data Fig. 5 A comparison of explanations.

We compare explanations from two common saliency methods (GradCAM [44] and GradCAM++ [45]) to a class activation visualization derived from our method. The explanations from IAIA-BL are more likely to highlight the lesion and less likely to highlight the surrounding healthy tissue. This is shown quantitatively by the activation precision metric. The single image visualization is a dramatic simplification of the full explanation that is generated by IAIA-BL. The IAIA-BL and ProtoPNet class activation visualizations shown in this figure are generated by taking the average of prototype activation maps for all prototypes of the correct class.

Extended Data Fig. 6 The architecture of the IAIA-BL prototype network.

Test image x feeds into convolutional layers f. Each patch of f(x)l is compared to each learned prototype pi by calculating the squared distance between the patch and the prototype. The similarity map shows the closest (most ‘activated,’ that is, smallest L2 distance) patches in red and the furthest patches in blue, overlaid on the test image. Similarity score si is calculated from the corresponding similarity map. The similarity scores s feed into fully connected layer h1, outputting margin logits \({\hat{{{{\bf{y}}}}}}^{{{\text{margin}}}}\). Margin logits \({\hat{{{{\bf{y}}}}}}^{{{\text{min}}}}\) feed into fully connected layer h2, outputting malignancy logit ymal.

Supplementary information

Supplementary Information

Supplementary Sections 1–10, Tables 1 and 2, and Figs. 1–6.

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Source data

Source Data Fig. 2

Labels and model predictions used to generate the ROC curves for Fig. 2.

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Barnett, A.J., Schwartz, F.R., Tao, C. et al. A case-based interpretable deep learning model for classification of mass lesions in digital mammography. Nat Mach Intell 3, 1061–1070 (2021). https://doi.org/10.1038/s42256-021-00423-x

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