  Histopathology whole slide images (WSIs) play a very important role in
clinical studies and serve as the gold standard for many cancer diagnoses.
However, generating automatic tools for processing WSIs is challenging due to
their enormous sizes. Currently, to deal with this issue, conventional methods
rely on a multiple instance learning (MIL) strategy to process a WSI at patch
level. Although effective, such methods are computationally expensive, because
tiling a WSI into patches takes time and does not explore the spatial relations
between these tiles. To tackle these limitations, we propose a locally
supervised learning framework which processes the entire slide by exploring the
entire local and global information that it contains. This framework divides a
pre-trained network into several modules and optimizes each module locally
using an auxiliary model. We also introduce a random feature reconstruction
unit (RFR) to preserve distinguishing features during training and improve the
performance of our method by 1% to 3%. Extensive experiments on three publicly
available WSI datasets: TCGA-NSCLC, TCGA-RCC and LKS, highlight the superiority
of our method on different classification tasks. Our method outperforms the
state-of-the-art MIL methods by 2% to 5% in accuracy, while being 7 to 10 times
faster. Additionally, when dividing it into eight modules, our method requires
as little as 20% of the total gpu memory required by end-to-end training. Our
code is available at https://github.com/cvlab-stonybrook/local_learning_wsi.
