Deep multimodal data fusion Sep 10, 2024 · Recent advancements in machine learning, particularly deep learning, have significantly advanced multimodal data fusion methods. In this paper, we propose a neural network-based multimodal data fusion framework named deep multimodal encoder (DME). However, effectively integrating multimodal neuroimaging data to enhance the accuracy of brain age estimation remains a challenging task. PET/CT fusion), to jointly analyzing in series where one modality informs another (e. Benjamin Ulfenborg is Associate Senior Lecturer at the Systems Biology Research Center, University of Skövde, Skövde, Sweden. In general May 1, 2020 · Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. Multimodal integration enables improved model accuracy and broader applicability by leveraging complementary information across different modalities, as well as facilitating knowledge Feb 5, 2025 · Advances in Multimodal Fusion of EHR and Medical Imaging Data Using deep learning techniques for advanced treatment of brain cancer Authors : Siva Raja P M , Vidhya S , Sumithra R P , Anjana S , Rejini K , Ramanan K Authors Info & Claims Feb 26, 2024 · 1. Jan 17, 2023 · The present work aims to develop a large-scale surface fuel identification model using a custom deep learning framework that can ingest multimodal data. Specifically, we use deep learning to extract information from multispectral signatures, high-resolution imagery, and biophysical climate and terrain data in a way that facilitates their end-to Mar 31, 2022 · Deep multimodal learning has achieved great progress in recent years. This paper applies these advantages by presenting a deep learning network architecture for 2. Multimodal deep learning (DL) in particular provides advantages over shallow methods for data fusion. May 1, 2024 · Second, recent studies have highlighted multimodal data fusion as promising research (Li et al. Multimodal Discriminative Conditional Apr 11, 2023 · The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. , 2023). Oct 20, 2024 · The multimodal approach, incorporating feature fusion, demonstrates superior performance in predicting crack initiation and propagation path compared to the unimodal approach, highlighting the significance of multimodal data fusion. Jan 10, 2025 · DOI: 10. Multimodal data fusion is an approach for combining single modalities to derive multimodal representation. Aug 1, 2024 · This study presents a comprehensive review of the most recent research and development trends of deep learning-based data fusion in cancer, with emphasis on the advancements of various data fusion methods based on heterogeneous data types (including specific methodologies, their pros, and cons), which offer substantial support for enhancing the Jun 26, 2022 · For example, Hua et al. Tremendous volumes of raw data are available and their smart management is the core concept of multimodal learning in a new paradigm for data fusion. The growing potential of multimodal data streams and deep learning algorithms has contributed to the increasing universality of deep multimodal learning. Yet, current methods including aggregation-based and alignment-based fusion are still inadequate in balancing the trade-off between inter-modal fusion and intra-modal Current challenges and future directions for multimodal data fusion. It also summarizes the current challenges and future topics of multimodal data fusion deep learning models. 2023. Philosophers and artists Jan 28, 2021 · When it comes to merging scores of these networks for the purpose of data fusion, the practical applications are limited by their complex process which lead to more computationally heavy processing and make them inapplicable for implementing on low-power systems. Jun 29, 2023 · Multimodal fusion integrates the complementary information present in multiple modalities and has gained much attention recently. Two of these challenges are learning from data with missing values, and finding shared representations for multimodal data to improve inference and prediction. 3. , radiological, pathological, and camera images Dec 13, 2024 · Background/Objectives: A multimodal brain age estimation model could provide enhanced insights into brain aging. , both traditional and deep learning-based schemes), multitask learning, multimodal alignment, multimodal transfer learning, and zero-shot learning. For instance, the personalized diagnosis and treatment planning for a single ca … Dec 3, 2021 · some methods for the fusion of multimodal data. Therefore, such techniques cannot be considered as low-level data fusion. Multimodal fusion with deep neural networks Mar 25, 2022 · The rapid development of diagnostic technologies in healthcare is leading to higher requirements for physicians to handle and integrate the heterogeneous, yet complementary data that are produced during routine practice. In this work, we propose dynamic multimodal fusion (DynMM), a new approach that adaptively fuses multimodal data In this work, we propose a novel deep learning-based framework for the fusion of multimodal data with heterogeneous dimensionality (e. Since the model capacity of traditional methods is limited, the performance improvements of multimodal biomedical data fusion has not been quickly for a period of time. May 3, 2022 · With the extremely rapid advances in remote sensing (RS) technology, a great quantity of Earth observation (EO) data featuring considerable and complicated heterogeneity is readily available nowadays, which renders researchers an opportunity to tackle current geoscience applications in a fresh way. Our proposed method extracts complex patterns and features The multimodal framework, multimodal medical data, and corresponding feature extraction were introduced, and the deep fusion methods were categorized and reviewed. Apr 24, 2024 · Teles A de Moura I Silva F Roberts A Stahl D (2025) EHR-based prediction modelling meets multimodal deep learning: A systematic review of structured and textual data fusion methods Information Fusion 10. In recent years, geologists have employed deep learning methods to develop comprehensive predictions of sedimentary facies. g. An increasing number of different sensor platforms are appearing in remote sensing, each of which can provide corresponding multimodal supplemental or enhanced information, such as optical images, light detection and ranging (LiDAR) point clouds, infrared images Mar 10, 2022 · Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex relationships among biological processes. Integrating multimodal data with fusion technologies allows more complementary information to be captured, which can help the prediction model increase its accuracy . Feb 24, 2024 · In this survey, we introduce the background and review the contemporary models of deep multimodal data fusion. L. Two deep Boltzmann machines (DBMs) are constructed for feature extraction from sensor data and nonlinear component-level model simulation data, respectively. , in examining diabetic retinopathy used fundus photography which was passed to a convolutional neural network (CNN) and then to a skip-connection deep network, while electronic health record data (EHR) was first passed to a skip-connection deep network [3]. Multitmodal models are powerful because they can integrate various Nov 15, 2024 · Effective lettuce cultivation requires precise monitoring of growth characteristics, quality assessment, and optimal harvest timing. Therefore Oct 17, 2024 · The search utilized keywords such as “data fusion”, “deep learning”, “remote sensing”, “neural networks”, “multimodal”, “multisource”, “optical and SAR”, “homogeneous”, “heterogeneous”, and “Change Detection”, which were carefully chosen to capture the essential concepts and methodologies associated with . Li et al [16] use principal component analysis (PCA) and Ding et al [17] use autoencoders to 🎆 🎆 🎆 Announcing the multimodal deep learning repository that contains implementation of various deep learning-based models to solve different multimodal problems such as multimodal representation learning, multimodal fusion for downstream tasks e. ca Olga Vechtomova University of Waterloo ovechtom@uwaterloo. Dynamic Fusion for Multimodal Data Gaurav Sahu University of Waterloo gaurav. Apr 24, 2024 · A new algorithm of data fusion using neural networks and Dempster-Shafer (D-S) evidence theory is presented in this paper to overcome these faults of data fusion, i. A spectrum of data fusion approaches. However, significant image disparities exist between HSI and LiDAR data because of their distinct imaging mechanisms, which limits the fusion of HSI and LiDAR data. Besides, the existing image segmentation datasets are summarized, covering 12 current multimodal datasets. Mar 18, 2020 · Request PDF | A Survey on Deep Learning for Multimodal Data Fusion | With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety Feb 10, 2025 · In this paper, we present a novel fusion technique, joint early pre-spatial fusion (JEPS), and evaluate its performance against single modality baseline models as well as established multimodal techniques for 2-year OS prediction on an internally sourced retrospective cohort of head and neck radiotherapy patients treated at our clinic. proposed the effect of vision on speech perception in 1976, which was used in Audio-Visual Speech Recognition (AVSR) technology [] and served as a prototype for the multimodal concep Feb 19, 2024 · Example architecture for intermediate/late multimodal fusion. Importantly, we systematically reviewed the last five years of deep learning-based multimodal cancer data fusion, focusing on the application of multimodal techniques to cancer survival prediction and subtype typing. 1007/s41060-025-00715-0 Corpus ID: 275451697; An overview of methods and techniques in multimodal data fusion with application to healthcare @article{Chaabene2025AnOO, title={An overview of methods and techniques in multimodal data fusion with application to healthcare}, author={Siwar Chaabene and Amal Boudaya and Bassem Bouaziz and Lotfi Cha{\^a}ri}, journal={International Journal of Jun 10, 2021 · The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. While this approach jointly mod-els the distribution of the audio and video data, it is limited as a shallow model. Go to reference in article; Crossref; Google Scholar [8] Deng S, Zhang X, Yan W, Chang E I-C, Fan Y, Lai M and Xu Y 2020 Deep learning in digital pathology image analysis: a survey Front. Feb 24, 2024 · This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multi-modality deep learning fusion method and to motivate new multimodAL data fusion techniques of deep learning. Consequently, the multimodal fusion technique outperformed single-modality sentiment analysis in terms of performance . This paper reviews the state-of-the-art methods for multimodal data fusion, which involves various types of data and feature engineering. The proposed framework extracts the features of the different modalities and projects them into the common feature subspace. This deep learning model aims to address two data-fusion problems: cross-modality and shared-modality representational learning. Use of multimodal data models is likely the only way to advance precision oncology, but many challenges exist to realise their full potential. In Amer, et al. With the rapid development of deep learning in recent years, multimodal fusion has become a popular topic. 3 Late Fusion in Deep Multimodal Learning. imu. A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges. e. Khaled Bayoudh, in Information Fusion, 2024. 2025. In this paper, we propose Jun 26, 2022 · For example, Hua et al. Go to reference in Aug 11, 2021 · We propose a compact and effective framework to fuse multimodal features at multiple layers in a single network. Such solutions may fail to fully capture the dynamics of interactions across modalities especially In this survey, we introduce the background and review the contemporary models of deep multimodal data fusion. In a recent study, a deep learning model based on multimodal data fusion was developed to estimate lettuce phenotypic traits accurately. Oct 15, 2024 · Personality traits influence an individual’s behavior, preferences and decision-making processes, making automated personality recognition an important area of research. 2011) for audio spectrograms and image fusion. Another study by Lee et al. Jan 1, 2021 · Then we categorized 20 deep multimodal fusion methods into early fusion, late fusion, and hybrid fusion. g Feb 20, 2017 · Multimodal Fusion Deep networks have been used for multimodal fusion (Srivastava and Salakhutdinov 2012) for tags and image fusion (Ngiam et al. Then the current pioneering multimodal data fusion deep learning models are summarized. 101367 Corpus ID: 263690924; Deep-learning-enabled multimodal data fusion for lung disease classification @article{Kumar2023DeeplearningenabledMD, title={Deep-learning-enabled multimodal data fusion for lung disease classification}, author={Sachin Kumar and Olga Ivanova and Artyom Melyokhin and Prayag Tiwari}, journal={Informatics in Medicine Unlocked}, year={2023}, url In this article, we reviewed recent advances in deep multimodal learning and organized them into six topics: multimodal data representation, multimodal fusion (i. Nov 6, 2024 · Moreover, compared to a single modality, the merged data hold more information. We provide a novel fine-grained taxonomy which groups SOTA multimodal data fusion methods into five categories: Encoder-Decoder Methods, Attention Mechanism Methods, GNN Methods, GenNN Methods, and other Constraint-based Methods. We review recent advances in deep multimodal learning and highlight the state-of the art, as well as gaps and challenges in this active research field. (2018), the authors proposed a hybrid approach as deep multimodal fusion to classify sequential data from multiple modalities. In recent years, several attention mechanisms have been introduced to enhance the performance of deep learning models. Through our new objective function, both the intra- and inter-modal correlations of multimodal sensor data can be better exploited for recovering the missing values May 1, 2020 · Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. McGurk et al. With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. Moreover, how to fully excavate and exploit the interactions between Feb 21, 2023 · Data processing in robotics is currently challenged by the effective building of multimodal and common representations. Deep learning (DL)-based data fusion strategies are a popular approach for modeling these nonlinear relationships. Fusion, in in increasing order of joint information provided, can range from simple visual inspection of two modalities (red and yellow circles), to overlaying them (e. Nov 26, 2024 · This survey offers a comprehensive review of recent advancements in multimodal alignment and fusion within machine learning, spurred by the growing diversity of data types such as text, images, audio, and video. Mar 31, 2021 · A Survey on Deep Learning for Multimodal Data Fusion With the wide deployments of heterogeneous networks, huge amounts of data with characteristics of high volume, high variety, high velocity, and high veracity are generated. A few issues should be taken into account when it comes to fusing several modalities: Intermodality: the combination of several modalities, which leads to better and more robust model predictions ; Oct 14, 2024 · In recent years, multimodal remote sensing data classification (MMRSC) has evoked growing attention due to its more comprehensive and accurate delineation of Earth’s surface compared to its single-modal counterpart. It combines information from various Dec 1, 2023 · Multisensor data fusion technology is one of the earliest forms of multimodal fusion to be applied in the field of aquaculture. By utilizing a stack-based shallow self-attention network, the model amplifies survival-related features within lesion regions. , 3D+2D) for segmentation tasks. In this paper, we propose a novel deep multimodal fusion for predicting personality traits from diverse data modalities, including text, audio, and visual inputs. Med. In particular, we described a new approach named multimodal encoder–decoder networks for efficient multitask learning with a shared feature representation. 1 focuses on fusing multimodal data at multiple scales. Oct 1, 2024 · Abstract page for arXiv paper 2410. Mar 8, 2017 · Heterogeneous sensor data fusion is a challenging field that has gathered significant interest in recent years. Dec 1, 2024 · Various deep learning-based multimodal biomedical data fusion methods have been proposed ranging from data-level fusion, feature-level fusion to decision-level fusion [15], [16]. To train a multimodal model, a direct approach is to train a RBM over the concatenated audio and video data (Figure 2c). Finally, some challenges and future topics of multimodal data fusion deep learning models are described. , Imon, B. Future research will focus on algorithm optimization for data fusion to improve feature extraction, and comparison with existing state-of-the-art methods to further improve the classification accuracy. Jan 28, 2022 · His research interests include machine learning, multimodal deep learning, data fusion and biomarker discovery. Nov 12, 2014 · This is a new hybrid fusion strategy based primarily on the implementation of two former and differentiated approaches to multimodal fusion [11] in multimodal dialogue systems. 32 829–64. Feb 27, 2024 · Currently, there exist some literature reviews regarding multimodal data fusion, which are summarized in table 2 according to different modality fusion. However, current fusion approaches are static in nature, i. Dec 6, 2020 · Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications. These models are generative, however, they operate on static data. Multimodal learning methods have made significant progress in several areas of intelligent information processing since the 1980s. 1016/j. 4. We provi de detailed real-world examples in manufacturing and med icine, introduce early, late, and inte rmediate fusion, as well as discuss sev- Aug 29, 2024 · Identifying sedimentary facies represents a fundamental aspect of oil and gas exploration. The framework shown in Fig. May 1, 2020 · Multimodal deep learning, presented by Ngiam et al. We further discussed architectural design to explore the essentials of deep multimodal fusion. Most existing fusion approaches either learn a fixed fusion strategy during training and inference, or are only capable of fusing the information to a certain extent. However, their methods are often constrained to some kind of unimodal data, and the practicality and generalizability of the resulting models are relatively limited. Feb 24, 2024 · Download Citation | Deep Multimodal Data Fusion | Multimodal Artificial Intelligence (Multimodal AI), in general, involves various types of data (e. 4 Multimodal fusion Jun 10, 2021 · The research progress in multimodal learning has grown rapidly over the last decade in several areas, especially in computer vision. However, it remains challenging to capture and integrate local and global features from single-modal data. , multimodal sentiment analysis. 00469: Deep Multimodal Fusion for Semantic Segmentation of Remote Sensing Earth Observation Data Accurate semantic segmentation of remote sensing imagery is critical for various Earth observation applications, such as land cover mapping, urban planning, and environmental monitoring. In recent years, the fusion-based classification of hyperspectral image (HSI) and light detection and ranging (LiDAR) data has garnered increasing attention from researchers. The framework projects the features of the modalities into a common subspace and achieves better results than monomodal methods on retinal imaging tasks. With the development of deep learning, researchers have proposed various deep learning algorithms based on multisensor data fusion to predict water quality parameters (Tang et al. We first classify deep multimodal learning architectures and then discuss methods to fuse Oct 12, 2024 · Similarly, within Guo et al. From the prior works, multimodal data typically yielded superior performance as compared with the unimodal data. Methods: In this study, we developed an innovative data fusion technique employing a low-rank tensor fusion algorithm, tailored specifically for Dec 1, 2024 · Handling multimodal lung cancer data using deep learning requires specialized frameworks that can effectively integrate and process data from diverse sources such as medical images, genomic data, clinical records, and more. Firstly, unlike existing multimodal methods that necessitate individual encoders for different modalities, we verify that multimodal features can be learnt within a shared single network by merely maintaining modality-specific Nov 9, 2017 · The success of deep learning has been a catalyst to solving increasingly complex machine-learning problems, which often involve multiple data modalities. , 3D+2D) that is compatible with localization tasks. His research interests are data fusion, data mining, machine learning and statistical modeling. (2021), a model for multimodal data fusion, named the multimodal affinity fusion network (MAFN), was introduced for predicting BC survival to integrate gene expression, CNA, and clinical data. However, current segmentation methods are limited to fusion of modalities with the same dimensionality (e. The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. A dual-modal network combining RGB and depth images was designed using an open lettuce dataset. For establishing an efficient multimodal deep learning framework, we attempt to predict DDIs based on different Dec 1, 2023 · Multimodal image fusion is challenging due to the heterogeneous nature of data, misalignment and nonlinear relationships between input data, or incomplete data during the fusion process. Therefore, we review the current state-of-the-a … Mar 8, 2024 · With the development of medical imaging technologies, breast cancer segmentation remains challenging, especially when considering multimodal imaging. These techniques can be categorized into early fusion, late fusion Dec 1, 2024 · Various deep learning-based multimodal biomedical data fusion methods have been proposed ranging from data-level fusion, feature-level fusion to decision-level fusion [15], [16]. Due to the complex socioeconomic UFZ properties, it is increasingly challenging to identify urban functional zones by using remote-sensing images (RSIs) alone. This involves the development of models capable of processing and analyzing the multimodal information to compare the results of our multimodal models, as well as for pre-training the deep networks. Multimodal Data Fusion. sahu@uwaterloo. fMRI seeded EEG reconstruction), to a full joint analysis of multimodal relationships. In this paper, we propose Dec 6, 2020 · Deep multimodal fusion by using multiple sources of data for classification or regression has exhibited a clear advantage over the unimodal counterpart on various applications. [10] used multimodal data fusion with fundus photographs and clinical risk factors to predict the risk of heart disease. With the joint utilization of EO data, much research on multimodal RS data fusion has made Nov 21, 2023 · Accurate and efficient classification maps of urban functional zones (UFZs) are crucial to urban planning, management, and decision making. Utilizing DL, various multimodal fusion techniques have been developed [18, 19]. Jan 1, 2025 · Considering that single-modal data cannot effectively and comprehensively reveal the CVDs and different physiological signals can provide complementary information about the heart, recently, some researchers have gradually paid their attention to the classification and prediction of CVDs using multimodal data and proved that the classification Nov 7, 2022 · These findings provide a summary on multimodal data fusion as applied to health diagnosis/prognosis problems. Apr 15, 2020 · This review paper presents some pioneering deep learning models to fuse multimodal big data, which contain abundant intermodality and cross-modality information. Image by roboflow. It covers a broad range of modalities, tasks, and challenges in this area, and provides a fine-grained taxonomy based on the main techniques used. The framework consists of two innovative fusion schemes. , 2020). A few of these DL Feb 1, 2023 · 2. 3 Data Fusion Based on Deep Learning. First, a deep semantic matching model is builded, which combines a deep neural network to fuse modal and matrix decomposition to deal with incomplete multimodal. For instance, the personalized diagnosis and treatment planning for a single cancer patient relies on the various images (e. In this paper, we propose amultimodal data fusion framework, the deep multimodal encoder (DME), based on deep learning Sep 23, 2021 · Extracting semantic information from very-high-resolution (VHR) aerial images is a prominent topic in the Earth observation research. Jan 1, 2023 · In this study, we put our full focus on biomedical data fusion. 2 Development of Multimodal Learning. Oct 1, 2023 · DOI: 10. Feb 2, 2024 · The use of multimodal imaging has led to significant improvements in the diagnosis and treatment of many diseases. inffus. g Mar 3, 2022 · This section proposes an incomplete multimodal data fusion algorithm based on deep semantic matching. Keywords: Multimodal learning · Multimodal fusion · Deep learning 1 Introduction The goal of multimodal learning is to learn and understand a variety of differ-ent types of information. Deep late fusion is a technique that combines the predictions of multiple neural networks that are trained on different modalities of data. Jan 29, 2020 · One method to improve deep multimodal fusion performance is to reduce the dimensionality of the data. The network incorporated both May 1, 2023 · In this paper, a novel DT approach based on deep multimodal information fusion (MIF) is proposed, which integrates information from the physical-based model (PBM) and the data-driven model. The multimodal fusion method of electronic health records based on deep learning can assist medical staff in the medical field to comprehensively analyze a large number of medical multimodal data generated in the process of diagnosis and treatment, thereby achieving accurate diagnosis and timely intervention for patients. Although data availability is the main driver of multimodal data fusion, it also poses its major barrier. is the most representative deep learning model based on the stacked autoencoder (SAE) for multimodal data fusion. , images, texts, or data collected from The evolution of architectures under different fusion classes is compared, highlighting their comparative advantages and limitations. Point-of-interest (POI) data and remote-sensing image data play important roles in UFZ Mar 18, 2020 · This review presents a survey on deep learning for multimodal data fusion to provide readers, regardless of their original community, with the fundamentals of multi-modality deep learning fusion method and to motivate new multimodAL data fusion techniques of deep learning. & Matthew, P. Specifically, representative architectures that are widely used are summarized as fundamental to the understanding of multimodal deep learning. According to the data fusion stage, multi-modal fusion has four primary methods: early fusion, deep fusion, late fusion, and hybrid fusion. Compared to a single-modality image, multimodal data provide additional information, contributing to better representation learning capabilities. 102981 118 (102981) Online publication date: Jun-2025 Jan 1, 2019 · In this chapter, we introduced several state-of-the-art approaches on deep learning for multimodal data fusion as well as basic techniques behind those works. , low accurate identification, bad stabilization and solution of uncertainty in some Feb 2, 2024 · The paper proposes a novel framework for fusing multimodal data with different dimensionality (e. Late fusion can be seen as a form of ensemble learning, where multiple models are combined to achieve better performance than individual models. Here, we cover these current challenges and reflect on opportunities through deep learning to tackle data sparsity and scarcity, multimodal interpretability, and standardisation of datasets. Therefore, establishing effective interaction (INTER Jan 1, 2023 · Model or hybrid level fusion uses the combination of feature level fusion and decision level fusion, in consideration of advantages of both these fusion strategies. Modality Dropout: Data Scarcity at Inference. 14 470–87. The paper surveys the three major multi-modal fusion technologies that can significantly enhance the effect of data fusion and further explore the applications of multi-modal fusion technology in various In this survey, we introduce the background and review the contemporary models of deep multimodal data fusion. Both approaches, their predecessors and their respective advantages and Apr 11, 2023 · [7] Gao J, Li P, Chen Z and Zhang J 2020 A survey on deep learning for multimodal data fusion Neural Comput. 2. Fully connected neural networks (FCNNs) are the conventional form of deep neural networks (DNNs) and can be viewed as a directed acyclical graph, which maps input to label through several hidden layers of nonlinear computational operations [ 12 ]. , 2022, Liu et al. Jan 1, 2023 · Kayikci and Khoshgoftaar [9] used multimodal deep learning for breast cancer prediction and claimed that the use of multimodal fusion has the potential to improve the diagnosis process. Although several techniques for building multimodal representations have been proven successful, they have not yet been However, many obstacles remain, including lack of usable data as well as methods for clinical validation and interpretation. ca Abstract Effective fusion of data from multiple modal-ities, such as video, speech, and text, is chal-lenging pertaining to the heterogeneous nature of multimodal data. Similar to clinical practice, some works have demonstrated the benefits of multimodal fusion for automatic segmentation and classification using deep learning-based methods. Roham, Z. Existing reviews either pay less attention to the direction of DL or only cover few sub-areas in multimodal RS data fusion, lacking a comprehensive and systematic description on this topic. , they process and fuse multimodal inputs with identical computation, without accounting for diverse computational demands of different multimodal data. Aug 1, 2022 · Currently, there exist some literature reviews regarding multimodal data fusion, which are summarized in Table 2 according to different modality fusion. vriwg edoan mhkj gmgqvgw vlzqoo wlnlb cmcz kehntsq cnly vbo vaxxm sckw htte xrlggb npsig