Homeomorphism Prior for False Positive and Negative Problem in Medical Image Dense Contrastive Representation Learning
Yuting He, Boyu Wang, Rongjun Ge, Yang Chen, Guanyu Yang, Shuo Li
2025-02-13
Summary
This paper talks about a new method called GEMINI, which helps AI systems better analyze medical images by reducing errors when matching parts of one image to another. It uses advanced techniques to make the process more accurate and efficient.
What's the problem?
AI systems often struggle when analyzing medical images because they make mistakes in matching similar areas between images. These mistakes, called false positives and false negatives, happen a lot due to the complexity of medical images, which can have overlapping details or low contrast. These errors reduce the accuracy of the AI's predictions and make it harder to trust its results.
What's the solution?
The researchers developed GEMINI, which uses a mathematical concept called homeomorphism to improve how AI matches parts of medical images. They introduced two main tools: Deformable Homeomorphism Learning (DHL), which helps the AI align images more accurately by predicting how parts of an image should move, and Geometric Semantic Similarity (GSS), which helps the AI measure how well different parts of the images match. These tools reduce errors and improve the AI's ability to learn from medical images.
Why it matters?
This matters because better analysis of medical images can lead to more accurate diagnoses and treatments. By reducing errors in how AI interprets these images, GEMINI could make medical tools more reliable and efficient, helping doctors work faster and with greater confidence. This improvement could ultimately save lives and reduce healthcare costs.
Abstract
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.