在醫學影像密集對比表示學習中針對偽陽性和偽陰性問題的同胚先驗

Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning

February 7, 2025

作者: Yuting He, Boyu Wang, Rongjun Ge, Yang Chen, Guanyu Yang, Shuo Li

cs.AI

摘要

密集對比表示學習（DCRL）已大大提高了圖像密集預測任務的學習效率，展示了它減少醫學圖像收集和密集標註的巨大成本的巨大潛力。然而，醫學圖像的特性使得不可靠的對應發現，帶來了 DCRL 中大規模假陽性和假陰性（FP＆N）對的一個開放問題。在本文中，我們提出了GEoMetric vIsual deNse sImilarity（GEMINI）學習，它在DCRL之前嵌入了同胚性先驗，實現了可靠的對應發現，以實現有效的密集對比。我們提出了一種可變同胚性學習（DHL），它對醫學圖像的同胚性進行建模，並學習估計可變形映射以預測像素的對應關係，實現拓撲保持。它有效地減少了配對的搜索空間，並通過梯度隱式和軟性地學習負對。我們還提出了一種幾何語義相似性（GSS），它提取特徵中的語義信息，用於衡量對應學習的對齊程度。這將促進變形的學習效率和性能，可靠地構建正對。我們在實驗中對兩個典型的表示學習任務實施了兩種實用變體。我們在七個數據集上取得了令人期待的結果，優於現有方法，展示了我們的優越性。我們將在以下鏈接上發布我們的代碼：https://github.com/YutingHe-list/GEMINI。

English

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.