Omics fusion has emerged as a crucial preprocessing approach in the field of medical image processing, providing significant assistance to several studies. One of the challenges encountered in the integration of omics data is the presence of unpredictability arising from disparities in data sources and medical imaging equipment. In order to overcome this challenge and facilitate the integration of their joint application to specific medical objectives, this study aims to develop a fusion methodology that mitigates the disparities inherent in omics data. The utilization of the multi-kernel late-fusion method has gained significant popularity as an effective strategy for addressing this particular challenge. An efficient representation of the data may be achieved by utilizing a suitable single-kernel function to map the inherent features and afterward merging them in a space with a high number of dimensions. This approach effectively addresses the differences noted before. The inflexibility of label fitting poses a constraint on the use of multi-kernel late-fusion methods in complex nasopharyngeal carcinoma (NPC) datasets, hence affecting the efficacy of general classifiers in dealing with high-dimensional characteristics. This innovative methodology aims to increase the disparity between the two cohorts, hence providing a more flexible structure for the allocation of labels. The examination of the NPC-ContraParotid dataset demonstrates the model's robustness and efficacy, indicating its potential as a valuable tool for predicting distant metastases in patients with nasopharyngeal carcinoma (NPC).
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation
The First People’s Hospital of Foshan, China（佛山市第一人民医院）；The Second Affiliated Hospital of Anhui Medical University, China（安徽医科大学第二附属医院）；Zhongshan Hospital, Fudan University, Shanghai, China（复旦大学中山医院）；ELTE University, Budapest, Hungary（ELTE大学）；Foundation Robotics Labs, Munich, Germany（Foundation Robotics Labs）
Multi-view Contrastive Learning with Additive Margin for Adaptive Nasopharyngeal Carcinoma Radiotherapy Prediction
The Hong Kong Polytechnic University（香港理工大学）；Research Institute for Smart Ageing, The Hong Kong Polytechnic University（香港理工大学智慧老龄研究学院）；Nantong University（南通大学）
Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scans
School of Software Engineering, Sun Yat-sen University（中山大学软件工程学院）；School of Microelectronics, Shanghai University（上海大学微电子学院）；Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；Ruijin Hospital, Shanghai Jiao Tong University School of Medicine（上海交通大学医学院附属瑞金医院）；Eye & ENT Hospital, Fudan University（复旦大学附属眼耳鼻喉科医院）
SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma
University of Electronic Science and Technology of China（电子科技大学）；Sichuan Cancer Hospital & Institute（四川省肿瘤医院（研究所））；Shanghai AI Lab（上海人工智能实验室）；Canon Medical Systems (China) Co. Ltd.（佳能医疗系统（中国）有限公司）；Stockholm University（斯德哥尔摩大学）；KTH（瑞典皇家理工学院）；Northwestern Polytechnical University（西北工业大学）；Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics
Fudan University Shanghai Cancer Center（复旦大学附属肿瘤医院）；Shanghai Medical College, Fudan University（复旦大学上海医学院）；Fudan University（复旦大学）；Shanghai Engineering Research Center of Molecular Imaging Probes（上海分子影像探针工程技术研究中心）；Key Laboratory of Nuclear Physics and Ion-beam Application (MOE), Fudan University（复旦大学核物理与离子束应用教育部重点实验室）；the University of Sydney（悉尼大学）；Shanghai Proton and Heavy Ion Center（上海市质子重离子医院）
Weighted Concordance Index Loss-based Multimodal Survival Modeling for Radiation Encephalopathy Assessment in Nasopharyngeal Carcinoma Radiotherapy
Harbin Institute of Technology（哈尔滨工业大学）；CVTE Research（CVTE研究院）；Southern University of Science and Technology（南方科技大学）；Sun Yat-sen University Cancer Center（中山大学肿瘤防治中心）
Primary Tumor and Inter-Organ Augmentations for Supervised Lymph Node Colon Adenocarcinoma Metastasis Detection
Department of Science and Technology, Linkoping University（林雪平大学科技学院）；Sectra AB（Sectra公司）；Center for Medical Image Science and Visualization, Linkoping University（林雪平大学医学影像科学与可视化中心）
A High Magnifications Histopathology Image Dataset for Oral Squamous Cell Carcinoma Diagnosis and Prognosis
School of Software Engineering, South China University of Technology（华南理工大学软件工程学院）；Department of Oral and Maxillofacial Surgery, Sun Yat-sen Memorial Hospital, Sun Yat-sen University（中山大学口腔颌面外科医院）；Mohamed Bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；School of Computer Science, University of Adelaide（阿德莱德大学计算机科学学院）
CDDSA: Contrastive Domain Disentanglement and Style Augmentation for Generalizable Medical Image Segmentation
University of Electronic Science and Technology of China（电子科技大学）；Shanghai AI Lab（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；ShanghaiTech University（上海科技大学）；Sensetime Research（商汤科技研究院）；Sichuan University（四川大学）；Sichuan Cancer Hospital and Institute（四川省肿瘤医院（四川省肿瘤研究所））
CmFNet: Cross-modal Fusion Network for Weakly-supervised Segmentation of Medical Images
School of Physics, Peking University（北京大学物理学院）；School of Computer Science, Peking University（北京大学计算机学院）；Department of Radiotherapy, Peking University Cancer Hospital（北京大学肿瘤医院放疗科）；Department of Radiation Oncology, Peking University Third Hospital（北京大学第三医院放疗科）
