Classification and Disease Localization in Histopathology Using Only Global Labels: A Weakly-Supervised Approach

TL;DR

This paper introduces a weakly-supervised deep learning method for disease localization in histopathology slides using only global labels, achieving performance comparable to fully-supervised models without requiring detailed annotations.

Contribution

It presents a novel approach combining pre-trained networks and multiple instance learning for effective disease localization with only image-level labels.

Findings

Achieved comparable performance to fully-supervised models on Camelyon-16.

Demonstrated effectiveness of weakly-supervised learning in histopathology.

Reduced need for detailed pixel-level annotations.

Abstract

Analysis of histopathology slides is a critical step for many diagnoses, and in particular in oncology where it defines the gold standard. In the case of digital histopathological analysis, highly trained pathologists must review vast whole-slide-images of extreme digital resolution ($100,000^2$ pixels) across multiple zoom levels in order to locate abnormal regions of cells, or in some cases single cells, out of millions. The application of deep learning to this problem is hampered not only by small sample sizes, as typical datasets contain only a few hundred samples, but also by the generation of ground-truth localized annotations for training interpretable classification and segmentation models. We propose a method for disease localization in the context of weakly supervised learning, where only image-level labels are available during training. Even without pixel-level annotations, we are able to demonstrate performance comparable with models trained with strong annotations on the Camelyon-16 lymph node metastases detection challenge. We accomplish this through the use of pre-trained deep convolutional networks, feature embedding, as well as learning via top instances and negative evidence, a multiple instance learning technique from the field of semantic segmentation and object detection.