An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion

• URL: http://arxiv.org/abs/2212.04661v1
• Date: Fri, 9 Dec 2022 04:19:43 GMT
• Title: An Attention-based Multi-Scale Feature Learning Network for Multimodal Medical Image Fusion
• Authors: Meng Zhou, Xiaolan Xu, Yuxuan Zhang
• Abstract summary: Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. We introduce a novel Dilated Residual Attention Network for the medical image fusion task.
• Score: 24.415389503712596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical images play an important role in clinical applications. Multimodal medical images could provide rich information about patients for physicians to diagnose. The image fusion technique is able to synthesize complementary information from multimodal images into a single image. This technique will prevent radiologists switch back and forth between different images and save lots of time in the diagnostic process. In this paper, we introduce a novel Dilated Residual Attention Network for the medical image fusion task. Our network is capable to extract multi-scale deep semantic features. Furthermore, we propose a novel fixed fusion strategy termed Softmax-based weighted strategy based on the Softmax weights and matrix nuclear norm. Extensive experiments show our proposed network and fusion strategy exceed the state-of-the-art performance compared with reference image fusion methods on four commonly used fusion metrics.

Related papers

• Fuse4Seg: Image-Level Fusion Based Multi-Modality Medical Image Segmentation [13.497613339200184]
  We argue the current feature-level fusion strategy is prone to semantic inconsistencies and misalignments. We introduce a novel image-level fusion based multi-modality medical image segmentation method, Fuse4Seg. The resultant fused image is a coherent representation that accurately amalgamates information from all modalities.
  arXiv  Detail & Related papers  (2024-09-16T14:39:04Z)
• A New Multimodal Medical Image Fusion based on Laplacian Autoencoder with Channel Attention [3.1531360678320897]
  Deep learning models have achieved end-to-end image fusion with highly robust and accurate performance. Most DL-based fusion models perform down-sampling on the input images to minimize the number of learnable parameters and computations. We propose a new multimodal medical image fusion model is proposed that is based on integrated Laplacian-Gaussian concatenation with attention pooling.
  arXiv  Detail & Related papers  (2023-10-18T11:29:53Z)
• Three-Dimensional Medical Image Fusion with Deformable Cross-Attention [10.26573411162757]
  Multimodal medical image fusion plays an instrumental role in several areas of medical image processing. Traditional fusion methods tend to process each modality independently before combining the features and reconstructing the fusion image. In this study, we introduce an innovative unsupervised feature mutual learning fusion network designed to rectify these limitations.
  arXiv  Detail & Related papers  (2023-10-10T04:10:56Z)
• A Task-guided, Implicitly-searched and Meta-initialized Deep Model for Image Fusion [69.10255211811007]
  We present a Task-guided, Implicit-searched and Meta- generalizationd (TIM) deep model to address the image fusion problem in a challenging real-world scenario. Specifically, we propose a constrained strategy to incorporate information from downstream tasks to guide the unsupervised learning process of image fusion. Within this framework, we then design an implicit search scheme to automatically discover compact architectures for our fusion model with high efficiency.
  arXiv  Detail & Related papers  (2023-05-25T08:54:08Z)
• M$^{2}$SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation [73.10707675345253]
  We propose a general multi-scale in multi-scale subtraction network (M$2$SNet) to finish diverse segmentation from medical image. Our method performs favorably against most state-of-the-art methods under different evaluation metrics on eleven datasets of four different medical image segmentation tasks.
  arXiv  Detail & Related papers  (2023-03-20T06:26:49Z)
• CoCoNet: Coupled Contrastive Learning Network with Multi-level Feature Ensemble for Multi-modality Image Fusion [72.8898811120795]
  We propose a coupled contrastive learning network, dubbed CoCoNet, to realize infrared and visible image fusion. Our method achieves state-of-the-art (SOTA) performance under both subjective and objective evaluation.
  arXiv  Detail & Related papers  (2022-11-20T12:02:07Z)
• Multimodal Information Fusion for Glaucoma and DR Classification [1.5616442980374279]
  Multimodal information is frequently available in medical tasks. By combining information from multiple sources, clinicians are able to make more accurate judgments. Our paper investigates three multimodal information fusion strategies based on deep learning to solve retinal analysis tasks.
  arXiv  Detail & Related papers  (2022-09-02T12:19:03Z)
• Coupled Feature Learning for Multimodal Medical Image Fusion [42.23662451234756]
  Multimodal image fusion aims to combine relevant information from images acquired with different sensors. In this paper, we propose a novel multimodal image fusion method based on coupled dictionary learning.
  arXiv  Detail & Related papers  (2021-02-17T09:13:28Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Cross-Modal Information Maximization for Medical Imaging: CMIM [62.28852442561818]
  In hospitals, data are siloed to specific information systems that make the same information available under different modalities. This offers unique opportunities to obtain and use at train-time those multiple views of the same information that might not always be available at test-time. We propose an innovative framework that makes the most of available data by learning good representations of a multi-modal input that are resilient to modality dropping at test-time.
  arXiv  Detail & Related papers  (2020-10-20T20:05:35Z)
• Hi-Net: Hybrid-fusion Network for Multi-modal MR Image Synthesis [143.55901940771568]
  We propose a novel Hybrid-fusion Network (Hi-Net) for multi-modal MR image synthesis. In our Hi-Net, a modality-specific network is utilized to learn representations for each individual modality. A multi-modal synthesis network is designed to densely combine the latent representation with hierarchical features from each modality.
  arXiv  Detail & Related papers  (2020-02-11T08:26:42Z)

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.