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.
@article{arxiv.1806.05083,
title = {Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology},
author = {Heather D. Couture and J. S. Marron and Charles M. Perou and Melissa A. Troester and Marc Niethammer},
journal= {arXiv preprint arXiv:1806.05083},
year = {2018}
}