  We focus on the problem of training convolutional neural networks on
gigapixel histopathology images to predict image-level targets. For this
purpose, we extend Neural Image Compression (NIC), an image compression
framework that reduces the dimensionality of these images using an encoder
network trained unsupervisedly. We propose to train this encoder using
supervised multitask learning (MTL) instead. We applied the proposed MTL NIC to
two histopathology datasets and three tasks. First, we obtained
state-of-the-art results in the Tumor Proliferation Assessment Challenge of
2016 (TUPAC16). Second, we successfully classified histopathological growth
patterns in images with colorectal liver metastasis (CLM). Third, we predicted
patient risk of death by learning directly from overall survival in the same
CLM data. Our experimental results suggest that the representations learned by
the MTL objective are: (1) highly specific, due to the supervised training
signal, and (2) transferable, since the same features perform well across
different tasks. Additionally, we trained multiple encoders with different
training objectives, e.g. unsupervised and variants of MTL, and observed a
positive correlation between the number of tasks in MTL and the system
performance on the TUPAC16 dataset.
