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, guiding the efficient representation learning for different
scale-level inter-image semantic features. Our experiments demonstrate that the
pre-training with our learning of inter-image similarity yields more powerful
inner-scene, inter-scene, and global-local transferring ability on four
challenging 3D medical image tasks. Our codes and pre-trained models will be
publicly available on this https URL.

提出新的几何学视觉相似性学习范式，将拓扑不变性的先验性质嵌入到 3D 医学图像的图像相似度测量中，以实现具有一致性的表示语义区域，从而提高内场、相场和全局 - 局部转移能力。