Coronavirus (Covid-19): Latest updates and information
Skip to main content Skip to navigation

Self Path: Self-supervision for Classification of Pathology Images with Limited Annotations

While high-resolution pathology images lend themselves well to 'data hungry' deep learning algorithms, obtaining exhaustive annotations on these images for learning is a major challenge. In this paper, we propose a self-supervised convolutional neural network (CNN) framework to leverage unlabeled data for learning generalizable and domain invariant representations in pathology images. Our proposed framework, termed as Self-Path, employs multi-task learning where the main task is tissue classification and pretext tasks are a variety of self-supervised tasks with labels inherent to the input images. We introduce novel pathology-specific self-supervision tasks that leverage contextual, multi-resolution and semantic features in pathology images for semi-supervised learning and domain adaptation. We investigate the effectiveness of Self-Path on 3 different pathology datasets. Our results show that Self-Path with the pathology-specific pretext tasks achieves state-of-the-art performance for semi-supervised learning when small amounts of labeled data are available. Further, we show that Self-Path improves domain adaptation for histopathology image classification when there is no labeled data available for the target domain. This approach can potentially be employed for other applications in computational pathology, where annotation budget is often limited or large amount of unlabeled image data is available.

Overview of Self-Path : The framework employs self-supervised pretext tasks. Pretext tasks can be added atop a shared encoder to learn useful representations and enhance semi-supervised learning or domain-adaptation. Green, red and blue lines indicate the flow of labeled, unlabeled and generated images, respectively. Generated images are used only for the generative task.

Click here to access the code.

Please cite our paper if you intend to use any part of the code in your research work.

Koohbanani, Navid Alemi, et al. "Self-Path: Self-supervision for Classification of Pathology Images with Limited Annotations." arXiv preprint arXiv:2008.05571 (2020).