Terabyte-scale Deep Multiple Instance Learning for Classification and Localization in Pathology

TL;DR

This paper introduces a large-scale deep learning framework for pathology that leverages a massive dataset and multiple instance learning to accurately diagnose prostate cancer from slides without pixel-level annotations.

Contribution

The study presents the first terabyte-scale pathology dataset and demonstrates a deep MIL approach achieving high accuracy in prostate cancer diagnosis.

Findings

Achieved an AUC of 0.98 on test set

Utilized a dataset 25 times larger than ImageNet

Enabled scalable training without pixel-wise labels

Abstract

In the field of computational pathology, the use of decision support systems powered by state-of-the-art deep learning solutions has been hampered by the lack of large labeled datasets. Until recently, studies relied on datasets in the order of few hundreds of slides which are not enough to train a model that can work at scale in the clinic. Here, we have gathered a dataset consisting of 12,160 slides, two orders of magnitude larger than previous datasets in pathology and equivalent to 25 times the pixel count of the entire ImageNet dataset. Given the size of our dataset it is possible for us to train a deep learning model under the Multiple Instance Learning (MIL) assumption where only the overall slide diagnosis is necessary for training, avoiding all the expensive pixel-wise annotations that are usually part of supervised learning approaches. We test our framework on a complex task, that of prostate cancer diagnosis on needle biopsies. We performed a thorough evaluation of the performance of our MIL pipeline under several conditions achieving an AUC of 0.98 on a held-out test set of 1,824 slides. These results open the way for training accurate diagnosis prediction models at scale, laying the foundation for decision support system deployment in the clinic.