title: Homeomorphism Prior for False Positive and Negative Problem in Medical
    Image Dense Contrastive Representation Learning
summary: 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.
paper_authors._id: 67ae4ddc8a3f2c111fa97e8d
paper_authors.name: Yuting He
paper_authors._id: 67ae4ddc8a3f2c111fa97e8e
paper_authors.name: Boyu Wang
paper_authors._id: 67ae4ddc8a3f2c111fa97e8f
paper_authors.name: Rongjun Ge
paper_authors._id: 67ae4ddc8a3f2c111fa97e90
paper_authors.name: Yang Chen
paper_authors._id: 67ae4ddc8a3f2c111fa97e91
paper_authors.name: Guanyu Yang
paper_authors._id: 67ae4ddc8a3f2c111fa97e92
paper_authors.name: Shuo Li
paper_title: Homeomorphism Prior for False Positive and Negative Problem in Medical
    Image Dense Contrastive Representation Learning
paper_summary: 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.
paper_ai_keywords: dense contrastive representations learning (DCRL) image-dense prediction tasks medical image collection dense annotation unreliable correspondence discovery large-scale false positive and negative (FP&N) pairs GEoMetric vIsual deNse sImilarity (GEMINI) learning homeomorphism prior deformable homeomorphism learning (DHL) homeomorphism of medical images deformable mapping topological preservation implicit and soft learning of negative pairs gradient geometric semantic similarity (GSS) semantic information in features alignment degree correspondence learning deformation positive pairs