Geometric Visual Similarity Learning In 3D Medical Image Self-supervised Pre-training
2023 · Yuting He, Guanyu Yang, Rongjun Ge, et al.
Abstract
Learning inter-image similarity is crucial for 3D medical images self-supervised pre-training, due to their sharing of numerous same semantic regions. However, the lack of the semantic prior in metrics and the semantic-independent variation in 3D medical images make it challenging to get a reliable measurement for the inter-image similarity, hindering the learning of consistent representation for same semantics. We investigate the challenging problem of this task, i.e., learning a consistent representation between images for a clustering effect of same semantic features. We propose a novel visual similarity learning paradigm, Geometric Visual Similarity Learning, which embeds the prior of topological invariance into the measurement of the inter-image similarity for consistent representation of semantic regions. To drive this paradigm, we further construct a novel geometric matching head, the Z-matching head, to collaboratively learn the global and local similarity of semantic regions,
Authors
(none)
Tags
Stats
Related papers
- Multi-view Dense Image Matching With Similarity Learning And Geometry Priors (2025)0.00
- Semantic Granularity Metric Learning For Visual Search (2019)7.81
- Cross-domain Visual Matching Via Generalized Similarity Measure And Feature Learning (2016)15.54
- Attention-based Dynamic Subspace Learners For Medical Image Analysis (2022)3.58
- Benchmarking Pretrained Vision Embeddings For Near- And Duplicate Detection In Medical Images (2023)7.16
- Selip: Similarity Enhanced Contrastive Language Image Pretraining For Multi-modal Head MRI (2025)3.58
- Region-based Contrastive Pretraining For Medical Image Retrieval With Anatomic Query (2023)0.00
- M3ret: Unleashing Zero-shot Multimodal Medical Image Retrieval Via Self-supervision (2025)0.00