Soft and self constrained clustering for group-based labeling

When using deep neural networks in medical image classification tasks, it is mandatory to prepare a large-scale labeled image set, and this often requires significant effort by medical experts. One strategy to reduce the labeling cost is group-based labeling, where image samples are clustered and then a label is attached to each cluster. The efficiency of this strategy depends on the purity of the clusters. Constrained clustering is an effective way to improve the purity of the clusters if we can give appropriate must-links and cannot-links as constraints. However, for medical image clustering, the conventional constrained clustering methods encounter two issues. The first issue is that constraints are not always appropriate due to the gap between semantic and visual similarities. The second issue is that attaching constraints requires extra effort from medical experts. To deal with the first issue, we propose a novel soft-constrained clustering method, which has the ability to ignore inappropriate constraints. To deal with the second issue, we propose a self-constrained clustering method that utilizes prior knowledge about the target images to set the constraints automatically. Experiments with the endoscopic image datasets demonstrated that the proposed methods give clustering results with higher purity. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

Utilizing group-based labeling and constrained clustering methods in medical image classification tasks can reduce labeling cost and improve clustering purity. However, challenges arise from inappropriate constraints and extra effort needed. To address these challenges, novel soft-constrained clustering and self-constrained clustering methods were proposed, achieving higher clustering purity in experiments with endoscopic image datasets.