Lesion detection serves a critical role in early diagnosis and has been well explored in recent years due to methodological advancesand increased data availability. However, the high costs of annotations hinder the collection of large and completely labeled datasets, motivating semi-supervised detection approaches. In this paper, we introduce mean teacher hetero-modal detection (MTHD), which addresses two important gaps in current semi-supervised detection. First, it is not obvious how to enforce unlabeled consistency constraints across the very different outputs of various detectors, which has resulted in various compromises being used in the state of the art. Using an anchor-free framework, MTHD formulates a mean teacher approach without such compromises, enforcing consistency on the soft-output of object centers and size. Second, multi-sequence data is often critical, e.g., for abdominal lesion detection, but unlabeled data is often missing sequences. To deal with this, MTHD incorporates hetero-modal learning in its framework. Unlike prior art, MTHD is able to incorporate an expansive set of consistency constraints that include geometric transforms and random sequence combinations. We train and evaluate MTHD on liver lesion detection using the largest MR lesion dataset to date (1099 patients with >5000 volumes). MTHD surpasses the best fully-supervised and semi-supervised competitors by 10.1% and 3.5%, respectively, in average sensitivity.
Weakly Supervised Visible-Infrared Person Re-Identification via Heterogeneous Expert Collaborative Consistency Learning
Faculty of Information Engineering and Automation, Kunming University of Science and Technology（昆明理工大学信息工程与自动化学院）；School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院）
Imagia Inc.（Imagia 公司）；Université de Sherbrooke（舍布鲁克大学）；Université de Montréal（蒙特利尔大学）
We introduce a deep learning image segmentation framework that is extremely robust to missing imaging modalities. Instead of attempting to impute or synthesize missing data, the proposed approach learns, for each modality, an embedding of the input image into a single latent vector space for which arithmetic operations (such as taking the mean) are well defined. Points in that space, which are averaged over modalities available at inference time, can then be further processed to yield the desired segmentation. As such, any combinatorial subset of available modalities can be provided as input, …
Mono-Modalizing Extremely Heterogeneous Multi-Modal Medical Image Registration
Department of Computer Science, Yonsei University（延世大学计算机科学系）；Department of Artificial Intelligence, Yonsei University（延世大学人工智能系）；Yonsei University College of Medicine（延世大学医学院）
Unified Multi-modal Diagnostic Framework with Reconstruction Pre-training and Heterogeneity-combat Tuning
The University of Hong Kong（香港大学）；City University of Hong Kong（香港城市大学）；The Hang Seng University of Hong Kong（香港恒生大学）；Chinese Academy of Sciences（中国科学院）
Duke University（杜克大学）；Geometric Data Analytics, Inc.（几何数据分析公司）；University of Wisconsin-Stout（威斯康星大学斯托特分校）；Air Force Research Laboratory（空军研究实验室）
In this work, we address the problem of cross-modal comparison of aerial data streams. A variety of simulated automobile trajectories are sensed using two different modalities: full-motion video, and radio-frequency (RF) signals received by detectors at various locations. The information represented by the two modalities is compared using self-similarity matrices (SSMs) corresponding to time-ordered point clouds in feature spaces of each of these data sources; we note that these feature spaces can be of entirely different scale and dimensionality. Several metrics for comparing SSMs are explore…
We propose a new deep learning method for tumour segmentation when dealing with missing imaging modalities. Instead of producing one network for each possible subset of observed modalities or using arithmetic operations to combine feature maps, our hetero-modal variational 3D encoder-decoder independently embeds all observed modalities into a shared latent representation. Missing data and tumour segmentation can be then generated from this embedding. In our scenario, the input is a random subset of modalities. We demonstrate that the optimisation problem can be seen as a mixture sampling. In a…
Towards Personalized Multi-Modal MRI Synthesis across Heterogeneous Datasets
University of Science and Technology of China（中国科学技术大学）；University of Electronic Science and Technology of China（电子科技大学）；Capital Medical University（首都医科大学）；Army Medical University（中国人民解放军陆军军医大学）；China National Clinical Research Center for Neurological Diseases（中国国家神经疾病临床研究中心）
Better with Less: Tackling Heterogeneous Multi-Modal Image Joint Pretraining via Conditioned and Degraded Masked Autoencoder
College of Electronic Science and Technology, National University of Defense Technology (NUDT)（电子科学与技术学院，国防科技大学）；Beijing Institute of Remote Sensing Information（遥感信息研究所）
Routine clinical visits of a patient produce not only image data, but also non-image data containing clinical information regarding the patient, i.e., medical data is multi-modal in nature. Such heterogeneous modalities offer different and complementary perspectives on the same patient, resulting in more accurate clinical decisions when they are properly combined. However, despite its significance, how to effectively fuse the multi-modal medical data into a unified framework has received relatively little attention. In this paper, we propose an effective graph-based framework called HetMed (He…
Zhejiang University（浙江大学）；University of Glasgow（格拉斯哥大学）
LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust multi-modal solution is under-explored, where we investigate three crucial inherent difficulties: modality heterogeneity, limited sensor field of view intersection, and multi-modal data augmentation. We propose a multi-modal 3D semantic segmentation model (MSeg3D) with joint intra-modal feature extraction and inter-modal feature fusion to mitigat…
Brain tissue segmentation from multimodal MRI is a key building block of many neuroscience analysis pipelines. It could also play an important role in many clinical imaging scenarios. Established tissue segmentation approaches have however not been developed to cope with large anatomical changes resulting from pathology. The effect of the presence of brain lesions, for example, on their performance is thus currently uncontrolled and practically unpredictable. Contrastingly, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly and is achieving …
Key Laboratory of Machine Perception, Peking University, Shenzhen Graduate School（北京大学深圳研究生院机器感知重点实验室）；Computer Vision Lab, ETH Zurich（苏黎世联邦理工学院计算机视觉实验室）
Multi-modal fusion is proven to be an effective method to improve the accuracy and robustness of speaker tracking, especially in complex scenarios. However, how to combine the heterogeneous information and exploit the complementarity of multi-modal signals remains a challenging issue. In this paper, we propose a novel Multi-modal Perception Tracker (MPT) for speaker tracking using both audio and visual modalities. Specifically, a novel acoustic map based on spatial-temporal Global Coherence Field (stGCF) is first constructed for heterogeneous signal fusion, which employs a camera model to map …
Institute of Computer Science and Technology, Peking University（北京大学计算机科学技术研究所）
It is known that the inconsistent distribution and representation of different modalities, such as image and text, cause the heterogeneity gap that makes it challenging to correlate such heterogeneous data. Generative adversarial networks (GANs) have shown its strong ability of modeling data distribution and learning discriminative representation, existing GANs-based works mainly focus on generative problem to generate new data. We have different goal, aim to correlate heterogeneous data, by utilizing the power of GANs to model cross-modal joint distribution. Thus, we propose Cross-modal GANs …
King’s College London（伦敦国王学院）；School of Biomedical Engineering & Imaging Sciences（生物医学工程与成像科学学院）；King’s College Hospital NHS Foundation Trust（国王学院医院国民健康服务基金会信托）；NVIDIA（英伟达）；Dementia Research Centre, UCL Institute of Neurology, UCL（伦敦大学学院痴呆研究中心、伦敦大学学院神经学研究所、伦敦大学学院）；Department of Medical Physics, UCL（伦敦大学学院医学物理系）；Department of Neuroradiology（神经放射科）；St. Thomas’ Hospital（圣托马斯医院）
Brain tissue segmentation from multimodal MRI is a key building block of many neuroimaging analysis pipelines. Established tissue segmentation approaches have, however, not been developed to cope with large anatomical changes resulting from pathology, such as white matter lesions or tumours, and often fail in these cases. In the meantime, with the advent of deep neural networks (DNNs), segmentation of brain lesions has matured significantly. However, few existing approaches allow for the joint segmentation of normal tissue and brain lesions. Developing a DNN for such a joint task is currently …
UCL（伦敦大学学院）；UCLH NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）
Any novel medical imaging modality that differs from previous protocols e.g. in the number of imaging channels, introduces a new domain that is heterogeneous from previous ones. This common medical imaging scenario is rarely considered in the domain adaptation literature, which handles shifts across domains of the same dimensionality. In our work we rely on stochastic generative modeling to translate across two heterogeneous domains at pixel space and introduce two new loss functions that promote semantic consistency. Firstly, we introduce a semantic cycle-consistency loss in the source domain…
Federated Prompt-Tuning with Heterogeneous and Incomplete Multimodal Client Data
Institute of AI Innovation and Societal Impact, Hanoi University of Science and Technology（人工智能创新与社会影响研究院，河内科学技术大学）；EPFL（瑞士联邦理工学院）；Griffith University（格里菲斯大学）；Washington State University（华盛顿州立大学）
Guangdong University of Technology（广东工业大学）；City University of Hong Kong（香港城市大学）；Huawei Noah’s Ark Lab（华为诺亚方舟实验室）；The Hong Kong Polytechnic University（香港理工大学）
In this work, we construct and release a multi-domain and multi-modality event dataset (MMED), containing 25,165 textual news articles collected from hundreds of news media sites (e.g., Yahoo News, Google News, CNN News.) and 76,516 image posts shared on Flickr social media, which are annotated according to 412 real-world events. The dataset is collected to explore the problem of organizing heterogeneous data contributed by professionals and amateurs in different data domains, and the problem of transferring event knowledge obtained from one data domain to heterogeneous data domain, thus summa…
