Homeomorphism Prior for False Positive and Negative Problem in Medical
  Image Dense Contrastive Representation Learning
Dense contrastive representation learning (DCRL) has greatly improved the
learning efficiency for image-dense prediction tasks, showing its great
potential to reduce the large costs of medical image collection and dense
annotation. However, the properties of medical images make unreliable
correspondence discovery, bringing an open problem of large-scale false
positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric
vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior
to DCRL and enables a reliable correspondence discovery for effective dense
contrast. We propose a deformable homeomorphism learning (DHL) which models the
homeomorphism of medical images and learns to estimate a deformable mapping to
predict the pixels' correspondence under topological preservation. It
effectively reduces the searching space of pairing and drives an implicit and
soft learning of negative pairs via a gradient. We also propose a geometric
semantic similarity (GSS) which extracts semantic information in features to
measure the alignment degree for the correspondence learning. It will promote
the learning efficiency and performance of deformation, constructing positive
pairs reliably. We implement two practical variants on two typical
representation learning tasks in our experiments. Our promising results on
seven datasets which outperform the existing methods show our great
superiority. We will release our code on a companion link:
https://github.com/YutingHe-list/GEMINI.
67ae4ddc8a3f2c111fa97e8d
Yuting He
67ae4ddc8a3f2c111fa97e8e
Boyu Wang
67ae4ddc8a3f2c111fa97e8f
Rongjun Ge
67ae4ddc8a3f2c111fa97e90
Yang Chen
67ae4ddc8a3f2c111fa97e91
Guanyu Yang
67ae4ddc8a3f2c111fa97e92
Shuo Li
Homeomorphism Prior for False Positive and Negative Problem in Medical
  Image Dense Contrastive Representation Learning
Dense contrastive representation learning (DCRL) has greatly improved the
learning efficiency for image-dense prediction tasks, showing its great
potential to reduce the large costs of medical image collection and dense
annotation. However, the properties of medical images make unreliable
correspondence discovery, bringing an open problem of large-scale false
positive and negative (FP&N) pairs in DCRL. In this paper, we propose GEoMetric
vIsual deNse sImilarity (GEMINI) learning which embeds the homeomorphism prior
to DCRL and enables a reliable correspondence discovery for effective dense
contrast. We propose a deformable homeomorphism learning (DHL) which models the
homeomorphism of medical images and learns to estimate a deformable mapping to
predict the pixels' correspondence under topological preservation. It
effectively reduces the searching space of pairing and drives an implicit and
soft learning of negative pairs via a gradient. We also propose a geometric
semantic similarity (GSS) which extracts semantic information in features to
measure the alignment degree for the correspondence learning. It will promote
the learning efficiency and performance of deformation, constructing positive
pairs reliably. We implement two practical variants on two typical
representation learning tasks in our experiments. Our promising results on
seven datasets which outperform the existing methods show our great
superiority. We will release our code on a companion link:
https://github.com/YutingHe-list/GEMINI.
GEMINI improves dense contrastive representation learning for medical images by incorporating deformable homeomorphism learning and geometric semantic similarity to enhance correspondence discovery, performance, and learning efficiency.
DCRL dense contrastive representation learning medical images homeomorphism prior correspondences deformable homeomorphism learning geometric semantic similarity topological preservation semantic information alignment degree