  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.
