D-LEMA: Deep Learning Ensembles from Multiple Annotations -- Application to Skin Lesion Segmentation

TL;DR

This paper introduces D-LEMA, a deep learning ensemble method that effectively utilizes multiple annotations per image to improve skin lesion segmentation accuracy, addressing annotator disagreement and confidence calibration.

Contribution

It presents a novel ensemble approach with Bayesian FCNs that handles multiple annotations and improves segmentation performance in medical imaging.

Findings

Outperforms existing methods on ISIC Archive dataset.

Enhances confidence calibration through model fusion.

Shows good generalization across different datasets.

Abstract

Medical image segmentation annotations suffer from inter- and intra-observer variations even among experts due to intrinsic differences in human annotators and ambiguous boundaries. Leveraging a collection of annotators' opinions for an image is an interesting way of estimating a gold standard. Although training deep models in a supervised setting with a single annotation per image has been extensively studied, generalizing their training to work with datasets containing multiple annotations per image remains a fairly unexplored problem. In this paper, we propose an approach to handle annotators' disagreements when training a deep model. To this end, we propose an ensemble of Bayesian fully convolutional networks (FCNs) for the segmentation task by considering two major factors in the aggregation of multiple ground truth annotations: (1) handling contradictory annotations in the training data originating from inter-annotator disagreements and (2) improving confidence calibration through the fusion of base models' predictions. We demonstrate the superior performance of our approach on the ISIC Archive and explore the generalization performance of our proposed method by cross-dataset evaluation on the PH2 and DermoFit datasets.