![]() In our different experiments, we employ a longitudinal dataset, named OPHDIAT, targeting diabetic retinopathy (DR) follow-up. This paper aims at providing a better understanding of those core algorithms for learning the disease progression with the mentioned change. We believe that there is an interesting connection to make between LSSL and NODE. Many temporal systems can be described by ODE, including modeling disease progression. NODE is a neural network architecture that learns the dynamics of ordinary differential equations (ODE) through the use of neural networks. Another new core framework named neural ordinary differential equation (NODE). Therefore, (as a first novelty) in this study, we explore the use of Siamese-like LSSL. However, conventional self-supervised strategies are usually implemented in a Siamese-like manner. The original LSSL is embedded in an auto-encoder (AE) structure. To better understand this core method, we explore in this paper the LSSL algorithm under different scenarios. By capturing temporal patterns without external labels or supervision, longitudinal self-supervised learning (LSSL) has become a promising avenue. In recent years, a novel class of algorithms has emerged with the goal of learning disease progression in a self-supervised manner, using either pairs of consecutive images or time series of images. Longitudinal analysis in medical imaging is crucial to investigate the progressive changes in anatomical structures or disease progression over time. Our source code will be made publicly available. The HSSL is compatible with various self-supervised methods, achieving superior performances on various downstream tasks, including image classification, semantic segmentation, instance segmentation, and object detection. This observation motivates us to propose a search strategy that quickly determines the most suitable auxiliary head for a specific base model to learn and several simple but effective methods to enlarge the model discrepancy. We discover that the representation quality of the base model moves up as their architecture discrepancy grows. To comprehensively understand the HSSL, we conduct experiments on various heterogeneous pairs containing a base model and an auxiliary head. In this process, HSSL endows the base model with new characteristics in a representation learning way without structural changes. Thus, we propose Heterogeneous Self-Supervised Learning (HSSL), which enforces a base model to learn from an auxiliary head whose architecture is heterogeneous from the base model. However, complementarity between such heterogeneous architectures has not been well exploited in self-supervised learning. Incorporating heterogeneous representations from different architectures has facilitated various vision tasks, e.g., some hybrid networks combine transformers and convolutions.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |