Relevant bibliographies by topics / Segmentation Multimodale

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Segmentation Multimodale'

Author: Grafiati
Published: 11 January 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Segmentation Multimodale.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Segmentation Multimodale"

1

Nai, Ying-Hwey, Bernice W. Teo, Nadya L. Tan, et al. "Evaluation of Multimodal Algorithms for the Segmentation of Multiparametric MRI Prostate Images." Computational and Mathematical Methods in Medicine 2020 (October 20, 2020): 1–12. http://dx.doi.org/10.1155/2020/8861035.

Full text
Abstract:
Prostate segmentation in multiparametric magnetic resonance imaging (mpMRI) can help to support prostate cancer diagnosis and therapy treatment. However, manual segmentation of the prostate is subjective and time-consuming. Many deep learning monomodal networks have been developed for automatic whole prostate segmentation from T2-weighted MR images. We aimed to investigate the added value of multimodal networks in segmenting the prostate into the peripheral zone (PZ) and central gland (CG). We optimized and evaluated monomodal DenseVNet, multimodal ScaleNet, and monomodal and multimodal HighRe
APA, Harvard, Vancouver, ISO, and other styles
2

Sun, Qixuan, Nianhua Fang, Zhuo Liu, Liang Zhao, Youpeng Wen, and Hongxiang Lin. "HybridCTrm: Bridging CNN and Transformer for Multimodal Brain Image Segmentation." Journal of Healthcare Engineering 2021 (October 1, 2021): 1–10. http://dx.doi.org/10.1155/2021/7467261.

Full text
Abstract:
Multimodal medical image segmentation is always a critical problem in medical image segmentation. Traditional deep learning methods utilize fully CNNs for encoding given images, thus leading to deficiency of long-range dependencies and bad generalization performance. Recently, a sequence of Transformer-based methodologies emerges in the field of image processing, which brings great generalization and performance in various tasks. On the other hand, traditional CNNs have their own advantages, such as rapid convergence and local representations. Therefore, we analyze a hybrid multimodal segmenta
APA, Harvard, Vancouver, ISO, and other styles
3

Pan, Mingyuan, Yonghong Shi, and Zhijian Song. "Segmentation of Gliomas Based on a Double-Pathway Residual Convolution Neural Network Using Multi-Modality Information." Journal of Medical Imaging and Health Informatics 10, no. 11 (2020): 2784–94. http://dx.doi.org/10.1166/jmihi.2020.3216.

Full text
Abstract:
The automatic segmentation of brain tumors in magnetic resonance (MR) images is very important in the diagnosis, radiotherapy planning, surgical navigation and several other clinical processes. As the location, size, shape, boundary of gliomas are heterogeneous, segmenting gliomas and intratumoral structures is very difficult. Besides, the multi-center issue makes it more challenging that multimodal brain gliomas images (such as T1, T2, fluid-attenuated inversion recovery (FLAIR), and T1c images) are from different radiation centers. This paper presents a multimodal, multi-scale, double-pathwa
APA, Harvard, Vancouver, ISO, and other styles
4

Desser, Dmitriy, Francisca Assunção, Xiaoguang Yan, Victor Alves, Henrique M. Fernandes, and Thomas Hummel. "Automatic Segmentation of the Olfactory Bulb." Brain Sciences 11, no. 9 (2021): 1141. http://dx.doi.org/10.3390/brainsci11091141.

Full text
Abstract:
The olfactory bulb (OB) has an essential role in the human olfactory pathway. A change in olfactory function is associated with a change of OB volume. It has been shown to predict the prognosis of olfactory loss and its volume is a biomarker for various neurodegenerative diseases, such as Alzheimer’s disease. Thus far, obtaining an OB volume for research purposes has been performed by manual segmentation alone; a very time-consuming and highly rater-biased process. As such, this process dramatically reduces the ability to produce fair and reliable comparisons between studies, as well as the pr
APA, Harvard, Vancouver, ISO, and other styles
5

Jain, Raunak, Faith Lee, Nianhe Luo, Harpreet Hyare, and Anand S. Pandit. "A Practical Guide to Manual and Semi-Automated Neurosurgical Brain Lesion Segmentation." NeuroSci 5, no. 3 (2024): 265–75. http://dx.doi.org/10.3390/neurosci5030021.

Full text
Abstract:
The purpose of the article is to provide a practical guide for manual and semi-automated image segmentation of common neurosurgical cranial lesions, namely meningioma, glioblastoma multiforme (GBM) and subarachnoid haemorrhage (SAH), for neurosurgical trainees and researchers. Materials and Methods: The medical images used were sourced from the Medical Image Computing and Computer Assisted Interventions Society (MICCAI) Multimodal Brain Tumour Segmentation Challenge (BRATS) image database and from the local Picture Archival and Communication System (PACS) record with consent. Image pre-process
APA, Harvard, Vancouver, ISO, and other styles
6

Zhu, Yuchang, and Nanfeng Xiao. "Simple Scalable Multimodal Semantic Segmentation Model." Sensors 24, no. 2 (2024): 699. http://dx.doi.org/10.3390/s24020699.

Full text
Abstract:
Visual perception is a crucial component of autonomous driving systems. Traditional approaches for autonomous driving visual perception often rely on single-modal methods, and semantic segmentation tasks are accomplished by inputting RGB images. However, for semantic segmentation tasks in autonomous driving visual perception, a more effective strategy involves leveraging multiple modalities, which is because different sensors of the autonomous driving system bring diverse information, and the complementary features among different modalities enhance the robustness of the semantic segmentation
APA, Harvard, Vancouver, ISO, and other styles
7

Farag, A. A., A. S. El-Baz, and G. Gimel'farb. "Precise segmentation of multimodal images." IEEE Transactions on Image Processing 15, no. 4 (2006): 952–68. http://dx.doi.org/10.1109/tip.2005.863949.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

You, Siming. "Deep learning in autonomous driving: Advantages, limitations, and innovative solutions." Applied and Computational Engineering 75, no. 1 (2024): 147–53. http://dx.doi.org/10.54254/2755-2721/75/20240528.

Full text
Abstract:
With the rapid development of autonomous driving technology, deep learning has become a core driver for innovation in testing autonomous driving scenarios. This review paper delves into the critical role of deep learning in autonomous driving technology. The paper will describe how deep learning is at the center of driving innovation. The paper thoroughly explores the application of deep learning in obstacle detection, scene classification and understanding, and image segmentation, emphasizing the significant benefits in perception and decision-making while pointing out the challenges and inno
APA, Harvard, Vancouver, ISO, and other styles
9

Zuo, Qiang, Songyu Chen, and Zhifang Wang. "R2AU-Net: Attention Recurrent Residual Convolutional Neural Network for Multimodal Medical Image Segmentation." Security and Communication Networks 2021 (June 10, 2021): 1–10. http://dx.doi.org/10.1155/2021/6625688.

Full text
Abstract:
In recent years, semantic segmentation method based on deep learning provides advanced performance in medical image segmentation. As one of the typical segmentation networks, U-Net is successfully applied to multimodal medical image segmentation. A recurrent residual convolutional neural network with attention gate connection (R2AU-Net) based on U-Net is proposed in this paper. It enhances the capability of integrating contextual information by replacing basic convolutional units in U-Net by recurrent residual convolutional units. Furthermore, R2AU-Net adopts attention gates instead of the ori
APA, Harvard, Vancouver, ISO, and other styles
10

Zhang, Yong, Yu-mei Zhou, Zhen-hong Liao, Gao-yuan Liu, and Kai-can Guo. "Artificial Intelligence-Guided Subspace Clustering Algorithm for Glioma Images." Journal of Healthcare Engineering 2021 (February 26, 2021): 1–9. http://dx.doi.org/10.1155/2021/5573010.

Full text
Abstract:
In order to improve the accuracy of glioma segmentation, a multimodal MRI glioma segmentation algorithm based on superpixels is proposed. Aiming at the current unsupervised feature extraction methods in MRI brain tumor segmentation that cannot adapt to the differences in brain tumor images, an MRI brain tumor segmentation method based on multimodal 3D convolutional neural networks (CNNs) feature extraction is proposed. First, the multimodal MRI is oversegmented into a series of superpixels that are uniform, compact, and exactly fit the image boundary. Then, a dynamic region merging algorithm b
APA, Harvard, Vancouver, ISO, and other styles

Dissertations / Theses on the topic "Segmentation Multimodale"

1

Bricq, Stéphanie. "Segmentation d’images IRM anatomiques par inférence bayésienne multimodale et détection de lésions." Université Louis Pasteur (Strasbourg) (1971-2008), 2008. https://publication-theses.unistra.fr/public/theses_doctorat/2008/BRICQ_Stephanie_2008.pdf.

Full text
Abstract:
L'imagerie médicale fournit un nombre croissant de données. La segmentation automatique est devenue une étape fondamentale pour l'analyse quantitative de ces images dans de nombreuses pathologies cérébrales comme la sclérose en plaques (SEP). Nous avons focalisé notre étude sur la segmentation d'IRM cérébrales. Nous avons d'abord proposé une méthode de segmentation des tissus cérébraux basée sur le modèle des chaînes de Markov cachées, permettant d'inclure l'information a priori apportée par un atlas probabiliste et prenant en compte les principaux artefacts présents sur les images IRM. Nous a
APA, Harvard, Vancouver, ISO, and other styles
2

Bricq, Stéphanie Collet Christophe Armspach Jean-Paul. "Segmentation d'images IRM anatomiques par inférence bayésienne multimodale et détection de lésions." Strasbourg : Université de Strasbourg, 2009. http://eprints-scd-ulp.u-strasbg.fr:8080/1143/01/BRICQ_Stephanie_2008-protege.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Toulouse, Tom. "Estimation par stéréovision multimodale de caractéristiques géométriques d’un feu de végétation en propagation." Thesis, Corte, 2015. http://www.theses.fr/2015CORT0009/document.

Full text
Abstract:
Les travaux menés dans cette thèse concernent le développement d'un dispositif de vision permettant l'estimation de caractéristiques géométriques d'un feu de végétation en propagation. Ce dispositif est composé de plusieurs systèmes de stéréovision multimodaux générant des paires d'images stéréoscopiques à partir desquelles des points tridimensionnels sont calculés et les caractéristiques géométriques de feu tels que sa position, vitesse, hauteur, profondeur, inclinaison, surface et volume sont estimées. La première contribution importante de cette thèse est la détection de pixels de feu de vé
APA, Harvard, Vancouver, ISO, and other styles
4

Kijak, Ewa. "Structuration multimodale des vidéos de sport par modèles stochastiques." Phd thesis, Université Rennes 1, 2003. http://tel.archives-ouvertes.fr/tel-00532944.

Full text
Abstract:
Cette étude présente une méthode de structuration d'une vidéo utilisant des indices sonores et visuels. Cette méthode repose sur un modèle statistique de l'entrelacement temporel des plans de la vidéo. Le cadre général de la modélisation est celui des modèles de Markov cachés. Les indices visuels sont utilisés pour caractériser le type des plans. Les indices audio décrivent les événements sonores apparaissant durant un plan. La structure de la vidéo est représentée par un modèle de Markov caché hiérarchique, intégrant les informations a priori sur le contenu de la vidéo, ainsi que sur les règl
APA, Harvard, Vancouver, ISO, and other styles
5

GAUTHIER, GERVAIS. "Applications de la morphologie mathematique fonctionnelle : analyse des textures en niveaux de gris et segmentation par approche multimodale." Caen, 1995. http://www.theses.fr/1995CAEN2050.

Full text
Abstract:
Les materiaux sont etudies par analyse de la texture interne donnant acces aux proprietes thermiques, electriques et mecaniques et par analyse de la forme externe (frottements et proprietes catalytiques). La premiere partie s'attache a la caracterisation de la forme externe. Les moyens d'observation sont presentes et critiques. Les differents parametres de mesure lies a la rugosite sont extraits soit de la surface, soit de profils verticaux, soit de sections horizontales. Leur caracterisation est insuffisante ; il est donc necessaire de recourir a l'emploi de fonctions d'abord de nature metriq
APA, Harvard, Vancouver, ISO, and other styles
6

Pham, Quoc Cuong. "Segmentation et mise en correspondance en imagerie cardiaque multimodale conduites par un modèle anatomique bi-cavités du coeur." Grenoble INPG, 2002. http://www.theses.fr/2002INPG0153.

Full text
Abstract:
L'imagerie cardiaque multimodale permet d'appréhender l'anatomie et les différents aspects fonctionnels du coeur, avec une précision croissante. Cette connaissance est essentielle dans le cadre de l'étude des pathologies ischémiques. Nous nous intéressons en premier lieu à l'extraction automatique de l'anatomie cardiaque à partir d'images par résonance magnétique. Notre approche de segmentation s'appuie sur l'utilisation d'un gabarit déformable élastique composé d'un modèle topologique et géométrique volumique des deux ventricules du coeur et d'un modèle mécanique de déformation élastique. Le
APA, Harvard, Vancouver, ISO, and other styles
7

Irace, Zacharie. "Modélisation statistique et segmentation d'images TEP : application à l'hétérogénéité et au suivi de tumeurs." Phd thesis, Toulouse, INPT, 2014. http://oatao.univ-toulouse.fr/12201/1/irace.pdf.

Full text
Abstract:
Cette thèse étudie le traitement statistique des images TEP. Plus particulièrement, la distribution binomiale négative est proposée pour modéliser l’activité d’une région mono-tissulaire. Cette représentation a l’avantage de pouvoir prendre en compte les variations d’activité biologique (ou hétérogénéité) d’un même tissu. A partir de ces résultats, il est proposé de modéliser la distribution de l’image TEP entière comme un mélange spatialement cohérent de lois binomiales négatives. Des méthodes Bayésiennes sont considérées pour la segmentation d’images TEP et l’estimation conjointe des paramèt
APA, Harvard, Vancouver, ISO, and other styles
8

Toulouse, Tom. "Estimation par stéréovision multimodale de caractéristiques géométriques d'un feu de végétation en propagation." Doctoral thesis, Université Laval, 2015. http://hdl.handle.net/20.500.11794/26472.

Full text
Abstract:
Les travaux menés dans cette thèse concernent le développement d’un dispositif de vision permettant l’estimation de caractéristiques géométriques d’un feu de végétation en propagation. Ce dispositif est composé de plusieurs systèmes de stéréovision multimodaux générant des paires d’images stéréoscopiques à partir desquelles des points tridimensionnels sont calculés et les caractéristiques géométriques de feu tels que sa position, vitesse, hauteur, profondeur, inclinaison, surface et volume sont estimées. La première contribution importante de cette thèse est la détection de pixels de feu de vé
APA, Harvard, Vancouver, ISO, and other styles
9

Baban, A. Erep Thierry Roland. "Contribution au développement d'un système intelligent de quantification des nutriments dans les repas d'Afrique subsaharienne." Electronic Thesis or Diss., Université de Toulouse (2023-....), 2024. http://www.theses.fr/2024TLSEP100.

Full text
Abstract:
La malnutrition, qu'elle soit liée à un apport insuffisant ou excessif en nutriments, représente un défi mondial de santé publique touchant des milliards de personnes. Elle affecte tous les systèmes organiques en étant un facteur majeur de risque pour les maladies non transmissibles telles que les maladies cardiovasculaires, le diabète et certains cancers. Évaluer l'apport alimentaire est crucial pour prévenir la malnutrition, mais cela reste un défi. Les méthodes traditionnelles d'évaluation alimentaire sont laborieuses et sujettes aux biais. Les avancées en IA ont permis la conception de VBD
APA, Harvard, Vancouver, ISO, and other styles
10

Ercolessi, Philippe. "Extraction multimodale de la structure narrative des épisodes de séries télévisées." Toulouse 3, 2013. http://thesesups.ups-tlse.fr/2056/.

Full text
Abstract:
Nos contributions portent sur l'extraction de la structure narrative d'épisodes de séries télévisées à deux niveaux hiérarchiques. Le premier niveau de structuration consiste à retrouver les transitions entre les scènes à partir d'une analyse de la couleur des images et des locuteurs présents dans les scènes. Nous montrons que l'analyse des locuteurs permet d'améliorer le résultat d'une segmentation en scènes basée sur la couleur. Il est courant de voir plusieurs histoires (ou lignes d'actions) racontées en parallèle dans un même épisode de série télévisée. Ainsi, le deuxième niveau de structu
APA, Harvard, Vancouver, ISO, and other styles

Books on the topic "Segmentation Multimodale"

1

Menze, Bjoern, and Spyridon Bakas, eds. Multimodal Brain Tumor Segmentation and Beyond. Frontiers Media SA, 2021. http://dx.doi.org/10.3389/978-2-88971-170-3.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Book chapters on the topic "Segmentation Multimodale"

1

Poulisse, Gert-Jan, and Marie-Francine Moens. "Multimodal News Story Segmentation." In Proceedings of the First International Conference on Intelligent Human Computer Interaction. Springer India, 2009. http://dx.doi.org/10.1007/978-81-8489-203-1_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Shah, Rajiv, and Roger Zimmermann. "Lecture Video Segmentation." In Multimodal Analysis of User-Generated Multimedia Content. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61807-4_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wang, Yaping, Hongjun Jia, Pew-Thian Yap, et al. "Groupwise Segmentation Improves Neuroimaging Classification Accuracy." In Multimodal Brain Image Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33530-3_16.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Dielmann, Alfred, and Steve Renals. "Multistream Dynamic Bayesian Network for Meeting Segmentation." In Machine Learning for Multimodal Interaction. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-30568-2_7.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Zhang, Daoqiang, Qimiao Guo, Guorong Wu, and Dinggang Shen. "Sparse Patch-Based Label Fusion for Multi-Atlas Segmentation." In Multimodal Brain Image Analysis. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33530-3_8.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Soldea, Octavian, Trung Doan, Andrew Webb, Mark van Buchem, Julien Milles, and Radu Jasinschi. "Simultaneous Brain Structures Segmentation Combining Shape and Pose Forces." In Multimodal Brain Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24446-9_18.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Poot, Dirk H. J., Marleen de Bruijne, Meike W. Vernooij, M. Arfan Ikram, and Wiro J. Niessen. "Improved Tissue Segmentation by Including an MR Acquisition Model." In Multimodal Brain Image Analysis. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24446-9_19.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Yu, Hao, Jie Zhao, and Li Zhang. "Vessel Segmentation via Link Prediction of Graph Neural Networks." In Multiscale Multimodal Medical Imaging. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18814-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Wang, Yi-Qing, and Giovanni Palma. "Liver Segmentation Quality Control in Multi-sequence MR Studies." In Multiscale Multimodal Medical Imaging. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-18814-5_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Cárdenes, Rubén, Meritxell Bach, Ying Chi, et al. "Multimodal Evaluation for Medical Image Segmentation." In Computer Analysis of Images and Patterns. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74272-2_29.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Segmentation Multimodale"

1

Wang, Zheng, Xinliang Zhang, and Junkun Zhao. "Sribble Supervised Multimodal Medical Image Segmentation." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650603.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Xia, Zhuofan, Dongchen Han, Yizeng Han, Xuran Pan, Shiji Song, and Gao Huang. "GSVA: Generalized Segmentation via Multimodal Large Language Models." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.00370.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Ahmad, Nisar, and Yao-Tien Chen. "3D Brain Tumor Segmentation in Multimodal MRI Images." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan). IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674099.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Dong, Shaohua, Yunhe Feng, Qing Yang, Yan Huang, Dongfang Liu, and Heng Fan. "Efficient Multimodal Semantic Segmentation via Dual-Prompt Learning." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801872.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Awudong, Buhailiqiemu, and Qi Li. "Improved Brain Tumor Segmentation Framework Based on Multimodal MRI and Cascaded Segmentation Strategy." In 2024 International Conference on Intelligent Computing and Data Mining (ICDM). IEEE, 2024. http://dx.doi.org/10.1109/icdm63232.2024.10762056.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Xu, Rongtao, Changwei Wang, Duzhen Zhang, et al. "DefFusion: Deformable Multimodal Representation Fusion for 3D Semantic Segmentation." In 2024 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610465.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Sankar, Shreeram, D. V. Santhosh Kumar, P. Kumar, and M. Rakesh Kumar. "Multimodal Fusion for Brain Medical Image Segmentation using MMSegNet." In 2024 4th International Conference on Intelligent Technologies (CONIT). IEEE, 2024. http://dx.doi.org/10.1109/conit61985.2024.10627205.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Han, Siyuan, Yao Wang, and Qian Wang. "Multimodal Medical Image Segmentation Algorithm Based on Convolutional Neural Networks." In 2024 Second International Conference on Networks, Multimedia and Information Technology (NMITCON). IEEE, 2024. http://dx.doi.org/10.1109/nmitcon62075.2024.10698930.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Sun, Yue, Zelong Zhang, Hong Shangguan, Jie Yang, Xiong Zhang, and Yuhuan Zhang. "A Multiscale Attention Multimodal Cooperative Learning Stroke Lesion Segmentation Network." In 2024 9th International Conference on Intelligent Computing and Signal Processing (ICSP). IEEE, 2024. http://dx.doi.org/10.1109/icsp62122.2024.10743876.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Huang, Chao, Weichao Cai, Qiuping Jiang, and Zhihua Wang. "Multimodal Representation Distribution Learning for Medical Image Segmentation." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/459.

Full text
Abstract:
Medical image segmentation is one of the most critical tasks in medical image analysis. However, the performance of existing methods is limited by the lack of high-quality labeled data due to the expensive data annotation. To alleviate this limitation, we propose a novel multi-modal learning method for medical image segmentation. In our method, medical text annotation is incorporated to compensate for the quality deficiency in image data. Moreover, previous multi-modal fusion methods ignore the commonalities and differences between different modalities. Ideally, the fused features should maxim
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography