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

Cluster-to-Conquer: A Framework for End-to-End Multi-Instance Learning for Whole Slide Image Classification