ArticleMedical image analysis2022
Cross-modal attention for multi-modal image registration.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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Who cites it
31 citing papers in PubMed.
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- A Multimodal Dense Parallel Global Attention Mechanism for Brain Tumor Image Segmentation.Journal of imaging · 2026Article
- Review
- Biologically Inspired Medical Multi-Modal Dataset Distillation via Contrast-Aware Alignment and Memory Compression.Biomimetics (Basel, Switzerland) · 2026Article
- Text guided cross attentive multimodal learning with visual feature modulation for automated skin lesion detection.Scientific reports · 2026Article
- Research on Colorization Algorithm for γ-Photon Flow Field Images Using the SECN Model.Entropy (Basel, Switzerland) · 2026Article
- Opportunities for Interventional Radiology in Transbronchial Interventions.Cardiovascular and interventional radiology · 2026Review
- Preoperative MRI-Guided Freehand Ultrasound Volume Reconstruction.Chemical & biomedical imaging · 2026Article
- A heatmap-based deep learning framework for multi-modal registration of VIS, NIR, and thermal images in dermatological imaging.Frontiers in artificial intelligence · 2026Article
- CT-based subchondral bone microstructural analysis in knee osteoarthritis via MR-guided distillation learning.Frontiers in radiology · 2026Article
- Cross-modal fusion of OCT and OCT angiography enface for improved diagnostics of diabetic retinopathy.Frontiers in medicine · 2026Article
- Multimodal natural language processing in ophthalmology: bridging clinical text and medical imaging.Frontiers in medicine · 2026Review
- Deep learning to predict future cognitive decline: a multimodal approach using brain MRI and clinical data.Frontiers in neuroimaging · 2026Article
- DINO-Reg: Efficient Multimodal Image Registration With Distilled Features.IEEE transactions on medical imaging · 2025Article
- E-CMCA and LSTM-Enhanced Framework for Cross-Modal MRI-TRUS Registration in Prostate Cancer.Journal of imaging · 2025Article
- Towards automatic US-MR fetal brain image registration with learning-based methods.NeuroImage · 2025Article
- A survey on deep learning in medical image registration: New technologies, uncertainty, evaluation metrics, and beyond.Medical image analysis · 2025Review
- TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.Medical image analysis · 2025Article
- [The dual-stream feature pyramid network based on Mamba and convolution for brain magnetic resonance image registration].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Multimodal deep learning approaches for precision oncology: a comprehensive review.Briefings in bioinformatics · 2024Review
Corrections and comments
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Authors and funding
10 authors.
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Abstract
In the past few years, convolutional neural networks (CNNs) have been proven powerful in extracting image features crucial for medical image registration. However, challenging applications and recent advances in computer vision suggest that CNNs are limited in their ability to understand the spatial correspondence between features, which is at the core of image registration. The issue is further exaggerated when it comes to multi-modal image registration, where the appearances of input images can differ significantly. This paper presents a novel cross-modal attention mechanism for correlating features extracted from the multi-modal input images and mapping such correlation to image registration transformation. To efficiently train the developed network, a contrastive learning-based pre-training method is also proposed to aid the network in extracting high-level features across the input modalities for the following cross-modal attention learning. We validated the proposed method on transrectal ultrasound (TRUS) to magnetic resonance (MR) registration, a clinically important procedure that benefits prostate cancer biopsy. Our experimental results demonstrate that for MR-TRUS registration, a deep neural network embedded with the cross-modal attention block outperforms other advanced CNN-based networks with ten times its size. We also incorporated visualization techniques to improve the interpretability of our network, which helps bring insights into the deep learning based image registration methods. The source code of our work is available at https://github.com/DIAL-RPI/Attention-Reg.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.