  In recent years, the availability of digitized Whole Slide Images (WSIs) has
enabled the use of deep learning-based computer vision techniques for automated
disease diagnosis. However, WSIs present unique computational and algorithmic
challenges. WSIs are gigapixel-sized ($\sim$100K pixels), making them
infeasible to be used directly for training deep neural networks. Also, often
only slide-level labels are available for training as detailed annotations are
tedious and can be time-consuming for experts. Approaches using
multiple-instance learning (MIL) frameworks have been shown to overcome these
challenges. Current state-of-the-art approaches divide the learning framework
into two decoupled parts: a convolutional neural network (CNN) for encoding the
patches followed by an independent aggregation approach for slide-level
prediction. In this approach, the aggregation step has no bearing on the
representations learned by the CNN encoder. We have proposed an end-to-end
framework that clusters the patches from a WSI into ${k}$-groups, samples
${k}'$ patches from each group for training, and uses an adaptive attention
mechanism for slide level prediction; Cluster-to-Conquer (C2C). We have
demonstrated that dividing a WSI into clusters can improve the model training
by exposing it to diverse discriminative features extracted from the patches.
We regularized the clustering mechanism by introducing a KL-divergence loss
between the attention weights of patches in a cluster and the uniform
distribution. The framework is optimized end-to-end on slide-level
cross-entropy, patch-level cross-entropy, and KL-divergence loss
(Implementation: https://github.com/YashSharma/C2C).
