Multiple Instance Learning for Heterogeneous Images: Training a CNN for Histopathology

Understand

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.