Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology
Multiple instance (MI) learning with a convolutional neural network enables end-to-end training in the presence of weak image-level labels. We propose a new method for aggregating predictions from smaller regions of the image into an image-level classification by using the quantile function. The quantile function provides a more complete description of the heterogeneity within each image, improving image-level classification. We also adapt image augmentation to the MI framework by randomly selecting cropped regions on which to apply MI aggregation during each epoch of training. This provides a mechanism to study the importance of MI learning. We validate our method on five different classification tasks for breast tumor histology and provide a visualization method for interpreting local image classifications that could lead to future insights into tumor heterogeneity.
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Heather D. Couture (add twitter)
J. S. Marron (add twitter)
Charles M. Perou (add twitter)
Melissa A. Troester (add twitter)
Marc Niethammer (add twitter)
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06/23/18 05:06PM
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