  In supervised learning for medical image analysis, sample selection
methodologies are fundamental to attain optimum system performance promptly and
with minimal expert interactions (e.g. label querying in an active learning
setup). In this paper we propose a novel sample selection methodology based on
deep features leveraging information contained in interpretability saliency
maps. In the absence of ground truth labels for informative samples, we use a
novel self supervised learning based approach for training a classifier that
learns to identify the most informative sample in a given batch of images. We
demonstrate the benefits of the proposed approach, termed
Interpretability-Driven Sample Selection (IDEAL), in an active learning setup
aimed at lung disease classification and histopathology image segmentation. We
analyze three different approaches to determine sample informativeness from
interpretability saliency maps: (i) an observational model stemming from
findings on previous uncertainty-based sample selection approaches, (ii) a
radiomics-based model, and (iii) a novel data-driven self-supervised approach.
We compare IDEAL to other baselines using the publicly available NIH chest
X-ray dataset for lung disease classification, and a public histopathology
segmentation dataset (GLaS), demonstrating the potential of using
interpretability information for sample selection in active learning systems.
Results show our proposed self supervised approach outperforms other approaches
in selecting informative samples leading to state of the art performance with
fewer samples.
