Affinity Propagation in Semi-Supervised Segmentation: A Biomedical Application

Given the scarcity of sufficient annotated data, using small sets of labeled samples under semi-supervision in biomedical imaging becomes necessary. Despite being highly successful, deep learning algorithms demand plenty of data to obtain significant performance. Complex data models make the usage of these methods costly. Selecting the correct model and tuning the hyperparameters of a model are also difficult jobs. Hence, a novel approach namely affinity propagation-based semi-supervised segmentation (APSS) is proposed. Here, affinity propagation clustering is modified and integrated with the advanced learning techniques that can efficiently use limited training data by discarding the completely exploited labeled data points. Moreover, a novel affinity calculation method is proposed considering both the Euclidean and geodesic distances to compute the distance between the two points on the histogram. This twofold contribution is tested using the three standard datasets (the International Skin Imaging Collaboration (ISIC) dermoscopic image dataset, the retinal fundus image dataset, and the liver tumor segmentation (LiTS) dataset). Results are compared with the three standard semi-supervised algorithms and four supervised algorithms. The effectiveness of the APSS approach in finding and exploiting the relationship between the labeled and unlabeled datasets is demonstrated in terms of qualitative (subjective evaluation and visual inspection) and quantitative performance (objective evaluation and numerical measurements).