Výsledky vyhledávání - "Multimodal medical image"

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Enhancing Multimodal Medical Image Classification using Cross-Graph Modal Contrastive Learning

Autor: Ding, Jun-En, Hsu, Chien-Chin, Liu, Feng

The classification of medical images is a pivotal aspect of disease diagnosis, often enhanced by deep learning techniques. However, traditional approaches typically focus on unimodal medical image data, neglecting the integration of diverse non-image

Externí odkaz: http://arxiv.org/abs/2410.17494

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multiGradICON: A Foundation Model for Multimodal Medical Image Registration

Autor: Demir, Basar, Tian, Lin, Greer, Thomas Hastings, Kwitt, Roland, Vialard, Francois-Xavier, Estepar, Raul San Jose, Bouix, Sylvain, Rushmore, Richard Jarrett, Ebrahim, Ebrahim, Niethammer, Marc

Modern medical image registration approaches predict deformations using deep networks. These approaches achieve state-of-the-art (SOTA) registration accuracy and are generally fast. However, deep learning (DL) approaches are, in contrast to conventio

Externí odkaz: http://arxiv.org/abs/2408.00221

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A Framework for Multimodal Medical Image Interaction

Autor: Schütz, Laura, Matinfar, Sasan, Schafroth, Gideon, Navab, Navid, Fairhurst, Merle, Wagner, Arthur, Wiestler, Benedikt, Eck, Ulrich, Navab, Nassir

Medical doctors rely on images of the human anatomy, such as magnetic resonance imaging (MRI), to localize regions of interest in the patient during diagnosis and treatment. Despite advances in medical imaging technology, the information conveyance r

Externí odkaz: http://arxiv.org/abs/2407.07015

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Robust Semi-supervised Multimodal Medical Image Segmentation via Cross Modality Collaboration

Autor: Zhou, Xiaogen, Sun, Yiyou, Deng, Min, Chu, Winnie Chiu Wing, Dou, Qi

Multimodal learning leverages complementary information derived from different modalities, thereby enhancing performance in medical image segmentation. However, prevailing multimodal learning methods heavily rely on extensive well-annotated data from

Externí odkaz: http://arxiv.org/abs/2408.07341

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A review of deep learning-based information fusion techniques for multimodal medical image classification

Autor: Li, Yihao, Daho, Mostafa El Habib, Conze, Pierre-Henri, Zeghlache, Rachid, Boité, Hugo Le, Tadayoni, Ramin, Cochener, Béatrice, Lamard, Mathieu, Quellec, Gwenolé

Multimodal medical imaging plays a pivotal role in clinical diagnosis and research, as it combines information from various imaging modalities to provide a more comprehensive understanding of the underlying pathology. Recently, deep learning-based mu

Externí odkaz: http://arxiv.org/abs/2404.15022

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Akademický článek

A multibranch and multiscale neural network based on semantic perception for multimodal medical image fusion

Autor: Cong Lin, Yinjie Chen, Siling Feng, Mengxing Huang

Publikováno v: Scientific Reports, Vol 14, Iss 1, Pp 1-23 (2024)

Abstract Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information

Externí odkaz: https://doaj.org/article/045d2abfad304192b06532377f18c073