Multi-contrast image clustering via multi-resolution augmentation and momentum-output queues

Contrastive clustering has emerged as an efficacious technique in the domain of deep clustering, leveraging the interplay between paired samples and the learning capabilities of deep network architectures. However, the augmentation strategies employed in the existing methods do not fully utilize the information of images, coupled with the limitation of the number of negative samples makes the clustering performance suffer. In this study, we propose a novel clustering approach that incorporates momentum-output queues and multi-resolution augmentation strategies to effectively address these limitations. Initially, we deploy a multi-resolution augmentation strategy, transforming conventional augmentations into distinct global and local perspectives across various resolutions. This approach comprehensively harnesses inter-image information to construct a multi-contrast model with multi-view inputs. Subsequently, we introduce momentum-output queues, which are designed to store a large number of negative samples without increasing the computational cost, thereby enhancing the clustering effect. Within our joint optimization framework, sample features are derived from both the original and momentum encoders for instance-level contrastive learning. Simultaneously, features produced exclusively by the original encoder within the same batch are employed for cluster-level contrastive learning. Our experimental results on five challenging datasets substantiate the superior performance of our method over existing state-of-the-art techniques.