DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation

TL;DR

This paper introduces DSAL, a deep active semi-supervised learning framework for biomedical image segmentation that efficiently selects samples for annotation using intermediate network features, reducing costs and improving performance.

Contribution

The paper proposes a novel sample selection criterion based on deep supervision, combining active and semi-supervised learning for biomedical image segmentation.

Findings

Outperforms state-of-the-art active learning methods on multiple datasets

Reduces computational costs through internal feature disagreement criteria

Effectively utilizes both strong and weak labelers in the annotation process

Abstract

Image segmentation is one of the most essential biomedical image processing problems for different imaging modalities, including microscopy and X-ray in the Internet-of-Medical-Things (IoMT) domain. However, annotating biomedical images is knowledge-driven, time-consuming, and labor-intensive, making it difficult to obtain abundant labels with limited costs. Active learning strategies come into ease the burden of human annotation, which queries only a subset of training data for annotation. Despite receiving attention, most of active learning methods generally still require huge computational costs and utilize unlabeled data inefficiently. They also tend to ignore the intermediate knowledge within networks. In this work, we propose a deep active semi-supervised learning framework, DSAL, combining active learning and semi-supervised learning strategies. In DSAL, a new criterion based on deep supervision mechanism is proposed to select informative samples with high uncertainties and low uncertainties for strong labelers and weak labelers respectively. The internal criterion leverages the disagreement of intermediate features within the deep learning network for active sample selection, which subsequently reduces the computational costs. We use the proposed criteria to select samples for strong and weak labelers to produce oracle labels and pseudo labels simultaneously at each active learning iteration in an ensemble learning manner, which can be examined with IoMT Platform. Extensive experiments on multiple medical image datasets demonstrate the superiority of the proposed method over state-of-the-art active learning methods.