NCF: Neural Correspondence Field for Medical Image Registration

• URL: http://arxiv.org/abs/2503.00760v1
• Date: Sun, 02 Mar 2025 06:55:49 GMT
• Title: NCF: Neural Correspondence Field for Medical Image Registration
• Authors: Lei Zhou, Nimu Yuan, Katjana Ehrlich, Jinyi Qi,
• Abstract summary: We propose a training-data-free learning-based method, Neural Correspondence Field (NCF), which can learn from just one data pair.<n>Our approach employs a compact neural network to model the correspondence field and optimize model parameters for each individual image pair.
• Score: 8.554246712772477
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Deformable image registration is a fundamental task in medical image processing. Traditional optimization-based methods often struggle with accuracy in dealing with complex deformation. Recently, learning-based methods have achieved good performance on public datasets, but the scarcity of medical image data makes it challenging to build a generalizable model to handle diverse real-world scenarios. To address this, we propose a training-data-free learning-based method, Neural Correspondence Field (NCF), which can learn from just one data pair. Our approach employs a compact neural network to model the correspondence field and optimize model parameters for each individual image pair. Consequently, each pair has a unique set of network weights. Notably, our model is highly efficient, utilizing only 0.06 million parameters. Evaluation results showed that the proposed method achieved superior performance on a public Lung CT dataset and outperformed a traditional method on a head and neck dataset, demonstrating both its effectiveness and efficiency.

Related papers

• Self-Supervised Learning in Deep Networks: A Pathway to Robust Few-Shot Classification [0.0]
  We first pre-train the model with self-supervision to enable it to learn common feature expressions on a large amount of unlabeled data. Then fine-tune it on the few-shot dataset Mini-ImageNet to improve the model's accuracy and generalization ability under limited data.
  arXiv  Detail & Related papers  (2024-11-19T01:01:56Z)
• Text-guided Foundation Model Adaptation for Long-Tailed Medical Image Classification [4.6651139122498]
  In medical contexts, the imbalanced data distribution in long-tailed datasets, due to scarce labels for rare diseases, greatly impairs the diagnostic accuracy of deep learning models. Recent multimodal text-image supervised foundation models offer new solutions to data scarcity through effective representation learning. We propose a novel Text-guided Foundation model Adaptation for Long-Tailed medical image classification (TFA-LT) Our method achieves an accuracy improvement of up to 27.1%, highlighting the substantial potential of foundation model adaptation in this area.
  arXiv  Detail & Related papers  (2024-08-27T04:18:18Z)
• A Simple and Efficient Baseline for Data Attribution on Images [107.12337511216228]
  Current state-of-the-art approaches require a large ensemble of as many as 300,000 models to accurately attribute model predictions. In this work, we focus on a minimalist baseline, utilizing the feature space of a backbone pretrained via self-supervised learning to perform data attribution. Our method is model-agnostic and scales easily to large datasets.
  arXiv  Detail & Related papers  (2023-11-03T17:29:46Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator [0.0]
  This paper proposes an Adaptive Data Augmentation Framework based on the tensor T-product Operator. It triples one image data to be trained and gain the result from all these three images together with only less than 0.1% increase in the number of parameters. Numerical experiments show that our data augmentation framework can improve the performance of original neural network model by 2%.
  arXiv  Detail & Related papers  (2023-06-07T08:30:44Z)
• Exploring Visual Prompts for Whole Slide Image Classification with Multiple Instance Learning [25.124855361054763]
  We present a novel, simple yet effective method for learning domain-specific knowledge transformation from pre-trained models to histopathology images. Our approach entails using a prompt component to assist the pre-trained model in discerning differences between the pre-trained dataset and the target histopathology dataset.
  arXiv  Detail & Related papers  (2023-03-23T09:23:52Z)
• Stacking Ensemble Learning in Deep Domain Adaptation for Ophthalmic Image Classification [61.656149405657246]
  Domain adaptation is effective in image classification tasks where obtaining sufficient label data is challenging. We propose a novel method, named SELDA, for stacking ensemble learning via extending three domain adaptation methods. The experimental results using Age-Related Eye Disease Study (AREDS) benchmark ophthalmic dataset demonstrate the effectiveness of the proposed model.
  arXiv  Detail & Related papers  (2022-09-27T14:19:00Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation [7.009650174262515]
  Federated learning (FL) enables collaborative model training while preserving each participant's privacy. FedAvg is a standard algorithm that uses fixed weights, often originating from the dataset sizes at each client, to aggregate the distributed learned models on a server during the FL process. In this work, we design a new data-driven approach, namely Auto-FedAvg, where aggregation weights are dynamically adjusted.
  arXiv  Detail & Related papers  (2021-04-20T18:29:44Z)
• A Multi-Stage Attentive Transfer Learning Framework for Improving COVID-19 Diagnosis [49.3704402041314]
  We propose a multi-stage attentive transfer learning framework for improving COVID-19 diagnosis. Our proposed framework consists of three stages to train accurate diagnosis models through learning knowledge from multiple source tasks and data of different domains. Importantly, we propose a novel self-supervised learning method to learn multi-scale representations for lung CT images.
  arXiv  Detail & Related papers  (2021-01-14T01:39:19Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.