Multiplex Graph Networks for Multimodal Brain Network Analysis
Lehigh University（理海大学）；Beihang University（北京航空航天大学）；University of Pittsburgh（匹兹堡大学）；Perelman School of Medicine（佩雷尔曼医学院）；University of Pennsylvania（宾夕法尼亚大学）
In this paper, we propose MGNet, a simple and effective multiplex graph convolutional network (GCN) model for multimodal brain network analysis. The proposed method integrates tensor representation into the multiplex GCN model to extract the latent structures of a set of multimodal brain networks, which allows an intuitive 'grasping' of the common space for multimodal data. Multimodal representations are then generated with multiplex GCNs to capture specific graph structures. We conduct classification task on two challenging real-world datasets (HIV and Bipolar disorder), and the proposed MGNet demonstrates state-of-the-art performance compared to competitive benchmark methods. Apart from objective evaluations, this study may bear special significance upon network theory to the understanding of human connectome in different modalities. The code is available at https://github.com/ZhaomingKong/MGNets.
MM-NeuroOnco: A Multimodal Benchmark and Instruction Dataset for MRI-Based Brain Tumor Diagnosis
Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Center for Brain-Inspired Computing Research (CBICR), Department of Precision Instrument, Tsinghua University（脑启发计算研究中心（CBICR）、精密仪器系，清华大学）
We apply an ensemble of modified TransBTS, nnU-Net, and a combination of both for the segmentation task of the BraTS 2021 challenge. In fact, we change the original architecture of the TransBTS model by adding Squeeze-and-Excitation blocks, an increasing number of CNN layers, replacing positional encoding in Transformer block with a learnable Multilayer Perceptron (MLP) embeddings, which makes Transformer adjustable to any input size during inference. With these modifications, we are able to largely improve TransBTS performance. Inspired by a nnU-Net framework we decided to combine it with our…
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Ningbo HwaMei Hospital, UCAS（中国科学院大学宁波华美医院）
Decoding human visual neural representations is a challenging task with great scientific significance in revealing vision-processing mechanisms and developing brain-like intelligent machines. Most existing methods are difficult to generalize to novel categories that have no corresponding neural data for training. The two main reasons are 1) the under-exploitation of the multimodal semantic knowledge underlying the neural data and 2) the small number of paired (stimuli-responses) training data. To overcome these limitations, this paper presents a generic neural decoding method called BraVL that…
Multimodal Recurrent Ensembles for Predicting Brain Responses to Naturalistic Movies (Algonauts 2025)
Max Planck Institute for Human Cognitive and Brain Sciences（马克斯·普朗克人类认知与脑科学研究所）；TU Dresden（德累斯顿技术大学）；School for Embedded and Composite AI (SECAI)（嵌入式与复合人工智能学院）；Center for Scalable Data Analytics & AI (ScaDS.AI)（可扩展数据与人工智能中心）
Reutlingen University（罗伊特林根大学）；Menoufia University（梅努菲亚大学）；Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院）
Automatic segmentation is essential for the brain tumor diagnosis, disease prognosis, and follow-up therapy of patients with gliomas. Still, accurate detection of gliomas and their sub-regions in multimodal MRI is very challenging due to the variety of scanners and imaging protocols. Over the last years, the BraTS Challenge has provided a large number of multi-institutional MRI scans as a benchmark for glioma segmentation algorithms. This paper describes our contribution to the BraTS 2022 Continuous Evaluation challenge. We propose a new ensemble of multiple deep learning frameworks namely, De…
ClusMFL: A Cluster-Enhanced Framework for Modality-Incomplete Multimodal Federated Learning in Brain Imaging Analysis
School of Science and Engineering, the Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳）理工学院）；Department of Computer Science and Engineering, Lehigh University（利哈伊大学计算机科学与工程系）；Department of Computer Science, Emory University（埃默里大学计算机科学系）
Istanbul Technical University（伊斯坦布尔理工大学）；National School for Computer Science (ENSI)（国立计算机科学学院）
Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain regions of interest (ROIs) and give insights into gender-related connectional differences. However, to the best of our knowledge, studies on gender differences in brain connectivity were limited to investigating pairwise (i.e., low-order) relationship ROIs, overlooking the complex high-order interconnectedness of the brain as a network. To address this limitation, brain multiplexes have been introduced to model the relatio…
mViSE: A Visual Search Engine for Analyzing Multiplex IHC Brain Tissue Images
Cullen College of Engineering, University of Houston（休斯顿大学塞勒恩工程学院）；The University of Texas McGovern Medical School（德克萨斯大学麦格芬医学学院）；National Institute of Neurological Disorders and Stroke（国家神经疾病与中风研究所）
Arizona State University（亚利桑那州立大学）；University of Pittsburgh（匹兹堡大学）；University of Southern California（南加州大学）
Applying network science approaches to investigate the functions and anatomy of the human brain is prevalent in modern medical imaging analysis. Due to the complex network topology, for an individual brain, mining a discriminative network representation from the multimodal brain networks is non-trivial. The recent success of deep learning techniques on graph-structured data suggests a new way to model the non-linear cross-modality relationship. However, current deep brain network methods either ignore the intrinsic graph topology or require a network basis shared within a group. To address the…
Weak-to-Strong Generalization Enables Fully Automated De Novo Training of Multi-head Mask-RCNN Model for Segmenting Densely Overlapping Cell Nuclei in Multiplex Whole-slice Brain Images
Department of Electrical and Computer Engineering, University of Houston（电子与计算机工程系，休斯顿大学）；National Institute of Neurological Disorders and Stroke（国家神经疾病与中风研究所）；Department of Neurobiology and Anatomy, UT McGovern Medical School（神经生物学与解剖学系，UT麦戈文医学学校）；Canva Pty Ltd.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Shenzhen University（深圳大学）；Renmin University of China（中国人民大学）；Harbin Engineering University（哈尔滨工程大学）
Using multimodal neuroimaging data to characterize brain network is currently an advanced technique for Alzheimer's disease(AD) Analysis. Over recent years the neuroimaging community has made tremendous progress in the study of resting-state functional magnetic resonance imaging (rs-fMRI) derived from blood-oxygen-level-dependent (BOLD) signals and Diffusion Tensor Imaging (DTI) derived from white matter fiber tractography. However, Due to the heterogeneity and complexity between BOLD signals and fiber tractography, Most existing multimodal data fusion algorithms can not sufficiently take adva…
University College London（伦敦大学学院）；University College London Hospitals（伦敦大学学院医院）；Wellcome/EPSRC Centre for Surgical and Interventional Science, UCL（惠康/EPSRC 外科与介入科学中心，伦敦大学学院）
Brain tumour segmentation plays a key role in computer-assisted surgery. Deep neural networks have increased the accuracy of automatic segmentation significantly, however these models tend to generalise poorly to different imaging modalities than those for which they have been designed, thereby limiting their applications. For example, a network architecture initially designed for brain parcellation of monomodal T1 MRI can not be easily translated into an efficient tumour segmentation network that jointly utilises T1, T1c, Flair and T2 MRI. To tackle this, we propose a novel scalable multimoda…
A Survey on fMRI-based Brain Decoding for Reconstructing Multimodal Stimuli
Beijing Institute of Basic Medical Sciences（北京基础医学研究所）；School of Computer Science and Information Engineering, Hefei University of Technology (HFUT)（合肥工业大学计算机与信息工程学院）；Institute of Artificial Intelligence, Hefei Comprehensive National Science Center（合肥综合性国家科学中心人工智能研究院）；Zhejiang University（浙江大学）；Australian Artificial Intelligence Institute, Faculty of Engineering and Information Technology, University of Technology Sydney（悉尼科技大学工程与信息技术学院澳大利亚人工智能研究所）；School of Information Science and Technology, University of Science and Technology of China（中国科学技术大学信息科学技术学院）
The Chinese University of Hong Kong（香港中文大学）；Imperial College Longdon（伦敦帝国理工学院）；Imsight Medical Technology Co. Ltd.（艾迈医疗科技有限公司）；The Hong Kong Polytechnic University（香港理工大学）；Chinese Academy of Sciences（中国科学院）
Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. However, in clinical practice, we often encounter the problem of missing imaging modalities. We tackle this challenge and propose a novel multimodal segmentation framework which is robust to the absence of imaging modalities. Our network uses feature disentanglement to decompose the input modalities into the modality-specific appearance code, which uniquely sticks to each modality, and the modality-invariant content code, which absorbs multimodal information for the s…
Brain-Inspired Multimodal Spiking Neural Network for Image-Text Retrieval
Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology（武汉理工大学交通物联网湖北省重点实验室）；State Key Laboratory for Multimedia Information Processing, Peking University（北京大学多媒体信息处理全国重点实验室）
Brain3D: EEG-to-3D Decoding of Visual Representations via Multimodal Reasoning
Università Politecnica delle Marche（马尔凯理工大学）；University of Macerata（马切拉塔大学）；Horizon Intelligence Labs（地平线智能实验室）；Harvard Medical School（哈佛医学院）；Massachusetts General Hospital（麻省总医院）
Image-to-Brain Signal Generation for Visual Prosthesis with CLIP Guided Multimodal Diffusion Models
Jinan University, Guangzhou, China；Department of Rehabilitation Medicine, The First Affiliated Hospital of Jinan University, Guangzhou, China；Western University, Ontario, Canada；Guangdong University of Technology, Guangzhou, China；Tsinghua University, Beijing, China
Graduate School at Shenzhen, Tsinghua University（清华大学深圳研究生院）；The University of Texas at Arlington（德克萨斯大学阿灵顿分校）
Accurate segmentation of brain tissue in magnetic resonance images (MRI) is a diffcult task due to different types of brain abnormalities. Using information and features from multimodal MRI including T1, T1-weighted inversion recovery (T1-IR) and T2-FLAIR and differential geometric features including the Jacobian determinant(JD) and the curl vector(CV) derived from T1 modality can result in a more accurate analysis of brain images. In this paper, we use the differential geometric information including JD and CV as image characteristics to measure the differences between different MRI images, w…
Massachusetts General Hospital（麻省总医院）；Massachusetts Institute of Technology（麻省理工学院）；National Taiwan University Hospital（台湾大学医院）；Vysioneer Inc.（Vysioneer公司）
Stereotactic radiosurgery (SRS), which delivers high doses of irradiation in a single or few shots to small targets, has been a standard of care for brain metastases. While very effective, SRS currently requires manually intensive delineation of tumors. In this work, we present a deep learning approach for automated detection and segmentation of brain metastases using multimodal imaging and ensemble neural networks. In order to address small and multiple brain metastases, we further propose a volume-aware Dice loss which optimizes model performance using the information of lesion size. This wo…
Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Tencent（腾讯）；University of California, Los Angeles（加州大学洛杉矶分校）；Lenovo Research（联想研究院）
Accurate brain tumor segmentation from Magnetic Resonance Imaging (MRI) is desirable to joint learning of multimodal images. However, in clinical practice, it is not always possible to acquire a complete set of MRIs, and the problem of missing modalities causes severe performance degradation in existing multimodal segmentation methods. In this work, we present the first attempt to exploit the Transformer for multimodal brain tumor segmentation that is robust to any combinatorial subset of available modalities. Concretely, we propose a novel multimodal Medical Transformer (mmFormer) for incompl…
In this paper, we propose a novel learning based method for automated segmenta-tion of brain tumor in multimodal MRI images. The machine learned features from fully convolutional neural network (FCN) and hand-designed texton fea-tures are used to classify the MRI image voxels. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI train-ing dataset using the FCN. The learned features are then applied to random for-ests to classify each MRI image voxel into normal brain tissues and different parts of tumor. The method was evaluated on BRATS 2013 …
Probing Multimodal Fusion in the Brain: The Dominance of Audiovisual Streams in Naturalistic Encoding
Distributed and Intelligent Optimization Research Laboratory（分布式智能优化研究实验室）；Electrical Engineering Department（电气工程系）；Amirkabir University of Technology（阿米尔卡比尔技术大学）
Chinese PLA General Hospital（中国人民解放军总医院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Peng Cheng Laboratory（鹏城实验室）；Beijing Jiaotong University（北京交通大学）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）
In neuroimaging analysis, fMRI can well assess the function changes for brain diseases with no obvious structural lesions. To date, most deep-learning-based fMRI studies have employed functional connectivity (FC) as the basic feature for disease classification. However, FC is calculated on time series of predefined regions of interest and neglects detailed information contained in each voxel. Another drawback of using FC is the limited sample size for the training of deep models. The low representation ability of FC leads to poor performance in clinical practice, especially when dealing with m…
Research & Development Institute of Northwestern Polytechnical University in Shenzhen（西北工业大学深圳研究院）；Northwestern Polytechnical University（西北工业大学）；University of Pittsburgh（匹兹堡大学）；University of Sydney（悉尼大学）；JD Finance America Corporation（京东金融美国公司）
In this paper, we propose a Hybrid High-resolution and Non-local Feature Network (H2NF-Net) to segment brain tumor in multimodal MR images. Our H2NF-Net uses the single and cascaded HNF-Nets to segment different brain tumor sub-regions and combines the predictions together as the final segmentation. We trained and evaluated our model on the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2020 dataset. The results on the test set show that the combination of the single and cascaded models achieved average Dice scores of 0.78751, 0.91290, and 0.85461, as well as Hausdorff distances ($95\%$…
BrainMAP: Multimodal Graph Learning For Efficient Brain Disease Localization
School of Computing Technologies, RMIT University（计算技术学院，拉筹纳斯大学）；Institute of Innovation, Science and Sustainability, Federation University Australia（创新、科学与可持续性研究所，联邦大学澳大利亚）
Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation
National Institute for Data Science in Health and Medicine（国家医学数据科学研究院）；Department of Computer Science at School of Informatics（信息学院计算机科学系）；Jarvis Research Center（Jarvis研究中心）；Medical Artificial Intelligence Lab（医学人工智能实验室）
The Brain Resection Multimodal Image Registration (ReMIND2Reg) 2025 Challenge
Inria Saclay Île-de-France（法国里昂萨克利研究所）；CEA（法国原子能委员会）；Université Paris-Saclay（巴黎-萨克利大学）；Sorbonne Université（索邦大学）；Institut du Cerveau - Paris Brain Institute - ICM（巴黎脑研究所）；CNRS（法国国家科学研究中心）；Inria（法国国家信息与自动化技术研究所）；Inserm（法国国家医学研究院）；AP-HP（法国国家医院集团）；Hôpital de la Pitié Salpêtrière（皮蒂埃-萨尔普里埃尔医院）；Harvard Medical School（哈佛医学院）；Brigham and Women's Hospital（布里根医院）；Department of Radiology and Radiological Science（放射学与放射科学系）；Johns Hopkins Medical School（约翰霍普金斯医学院）；Institute of Medical Informatics（医学信息学研究所）；University of Lübeck（吕贝克大学）；Image Analysis and Communications Laboratory（图像分析与通信实验室）；Department of Electrical and Computer Engineering（电气与计算机工程系）；Johns Hopkins University（约翰霍普金斯大学）；CSAIL（媒体实验室）
UPMAD-Net: A Brain Tumor Segmentation Network with Uncertainty Guidance and Adaptive Multimodal Feature Fusion
School of Computer Science and Technology, Zhengzhou University of Light Industry（郑州轻工业大学计算机与通信工程学院）；Kennesaw State University（肯尼索州立大学）；Michigan Technological University（密歇根理工大学）
University of Helsinki（赫尔辛基大学）；Artificial Intelligence Research Institute（人工智能研究院）
Supporting the current trend in the AI community, we present the AI Journey 2021 Challenge called Fusion Brain, the first competition which is targeted to make the universal architecture which could process different modalities (in this case, images, texts, and code) and solve multiple tasks for vision and language. The Fusion Brain Challenge combines the following specific tasks: Code2code Translation, Handwritten Text recognition, Zero-shot Object Detection, and Visual Question Answering. We have created datasets for each task to test the participants' submissions on it. Moreover, we have co…
South China University of Technology（华南理工大学）；University of Utah（犹他大学）
In medical applications, the same anatomical structures may be observed in multiple modalities despite the different image characteristics. Currently, most deep models for multimodal segmentation rely on paired registered images. However, multimodal paired registered images are difficult to obtain in many cases. Therefore, developing a model that can segment the target objects from different modalities with unpaired images is significant for many clinical applications. In this work, we propose a novel two-stream translation and segmentation unified attentional generative adversarial network (U…
OmniBrainBench: A Comprehensive Multimodal Benchmark for Brain Imaging Analysis Across Multi-stage Clinical Tasks
Chinese University of Hong Kong（香港中文大学）；Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学孙逸仙纪念医院）；Southern Medical University（南方医科大学）；Zhongshan Hospital, Fudan University（复旦大学中山医院）；Department of Neurosurgery, Prince of Wales Hospital（威尔士王室医院神经外科部）
Towards General Text-guided Image Synthesis for Customized Multimodal Brain MRI Generation
ShanghaiTech University（上海科技大学）；Shanghai Clinical Research and Trial Center（上海临床研究与试验中心）；Affiliated Hangzhou First People’s Hospital, Xihu University School of Medicine（西湖大学医学院附属杭州市第一人民医院）
Disentangled Multimodal Brain MR Image Translation via Transformer-based Modality Infuser
Gordon Center for Medical Imaging, Massachusetts General Hospital and Harvard Medical School（马萨诸塞州总医院与哈佛医学院戈登医学影像中心）；Korea Advanced Institute of Science and Technology（韩国科学技术院）
School of Informatics, Xiamen University（厦门大学信息学院）；Tencent Jarvis Lab, Tencent Healthcare (Shenzhen) Co., Ltd.（腾讯医疗健康（深圳）有限公司腾讯杰维斯实验室）；School of Medicine, Xiamen University（厦门大学医学院）
Multimodal magnetic resonance imaging (MRI) provides complementary information for sub-region analysis of brain tumors. Plenty of methods have been proposed for automatic brain tumor segmentation using four common MRI modalities and achieved remarkable performance. In practice, however, it is common to have one or more modalities missing due to image corruption, artifacts, acquisition protocols, allergy to contrast agents, or simply cost. In this work, we propose a novel two-stage framework for brain tumor segmentation with missing modalities. In the first stage, a multimodal masked autoencode…
Biological Brain Age Estimation using Sex-Aware Adversarial Variational Autoencoder with Multimodal Neuroimages
Dongguk University（东国大学）；Seoul National University（首尔大学）；Stanford University（斯坦福大学）；DeepChain AI&IT Technologies（DeepChain AI与IT技术公司）；DeepNoid Inc.（DeepNoid公司）；Soonchunhyang University Bucheon Hospital（顺天乡大学富川医院）；Naif Arab University for Security Sciences（奈夫阿拉伯安全科学大学）；University of New South Wales（新南威尔士大学）
University of Catania（卡塔尼亚大学）；University of Central Florida（中佛罗里达大学）
This work presents a novel method of exploring human brain-visual representations, with a view towards replicating these processes in machines. The core idea is to learn plausible computational and biological representations by correlating human neural activity and natural images. Thus, we first propose a model, EEG-ChannelNet, to learn a brain manifold for EEG classification. After verifying that visual information can be extracted from EEG data, we introduce a multimodal approach that uses deep image and EEG encoders, trained in a siamese configuration, for learning a joint manifold that max…
