Machine learning techniques are being increasingly applied in medical and physical sciences across a variety of imaging modalities; however, an important issue when developing these tools is the availability of good quality training data. Here we present a unique, multimodal synchrotron dataset of a bespoke zinc-doped Zeolite 13X sample that can be used to develop advanced deep learning and data fusion pipelines. Multi-resolution micro X-ray computed tomography was performed on a zinc-doped Zeolite 13X fragment to characterise its pores and features, before spatially resolved X-ray diffraction computed tomography was carried out to characterise the homogeneous distribution of sodium and zinc phases. Zinc absorption was controlled to create a simple, spatially isolated, two-phase material. Both raw and processed data is available as a series of Zenodo entries. Altogether we present a spatially resolved, three-dimensional, multimodal, multi-resolution dataset that can be used for the development of machine learning techniques. Such techniques include development of super-resolution, multimodal data fusion, and 3D reconstruction algorithm development.
Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification
College of Engineering and Computer Science, VinUni-Illinois Smart Health Center, VinUniversity（工程与计算机科学学院，VinUni - 伊利诺伊智能健康中心，Vin大学）；School of Biomedical Engineering, International University, Vietnam National University HCMC（生物医学工程学院，胡志明市越南国立大学国际大学）
MEWL: Few-shot multimodal word learning with referential uncertainty
Peking University（北京大学）；Beijing Institute for General Artificial Intelligence（北京通用人工智能研究院）；Beijing Institute of Technology（北京理工大学）；Yangtze Delta Region Academy of Beijing Institute of Technology（北京理工大学长三角研究院）
Automated Ensemble Multimodal Machine Learning for Healthcare
Department of Statistics, University of Oxford（统计系，牛津大学）；Division of Medical Image Computing, German Cancer Research Center (DKFZ)（医学影像计算部，德国癌症研究中心（DKFZ））；Medical Faculty Heidelberg, Heidelberg University（海德堡大学医学系）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）；Pattern Analysis and Learning Group, Department of Radiation Oncology, Heidelberg University Hospital（模式分析与学习小组，海德堡大学医院放射肿瘤学系）；National Center for Tumor Diseases (NCT) Heidelberg（海德堡国家肿瘤疾病中心（NCT））；Department of Applied Mathematics and Theoretical Physics, University of Cambridge（应用数学与理论物理系，剑桥大学）
Review of multimodal machine learning approaches in healthcare
Oxford Internet Institute, University of Oxford（牛津大学互联网研究所）；Institute for People-Centred AI, University of Surrey（萨里大学以人为本人工智能研究所）；Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London（伦敦大学学院惠康/英国工程与物理科学研究委员会介入与外科科学中心）
Machine Learning for Multimodal Electronic Health Records-based Research: Challenges and Perspectives
Peng Cheng Laboratory（鹏城实验室）；Carnegie Mellon University（卡内基梅隆大学）；University of Toronto Mississauga（多伦多大学密西沙加分校）；Peking University（北京大学）
背景资料：电子健康档案（Electronic Health Records，EHR）包含了丰富的患者健康历史信息，通常包括结构化和非结构化数据。已经有许多研究专注于从结构化数据中提取有价值的信息，例如疾病代码，实验室测试结果和治疗方法。然而，单靠结构化数据未必能全面反映病人的数据，而这些数据间中亦可能有错误的纪录。目的：随着机器学习（ML）和深度学习（DL）技术的最新进展，越来越多的研究试图通过合并非结构化自由文本数据来获得更准确的结果。本文回顾了使用多模态数据（即结构化和非结构化数据的组合）的研究，这些数据来自EHR，作为传统ML或DL模型的输入，以解决目标任务。材料与方法：我们在电气和电子工程师协会（IEEE）数字图书馆、PubMed和计算机械协会（ACM）数字图书馆中检索了与基于ML的多模态EHR研究相关的文章。结果和讨论：在最后的94项研究中，我们重点关注来自不同模态的数据如何使用传统的ML和DL技术进行组合和交互，以及这些算法如何应用于EHR相关任务。此外，我们调查这些融合方法的优点和局限性，并指出未来的方向ML为基础的多模态EHR研究。
A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms
Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Wisconsin School of Business, University of Wisconsin–Madison（威斯康星大学麦迪逊分校商学院）；Heart and Vascular Institute, Hartford Hospital（哈特福德医院心脏与血管研究院）
Early detection of inflammatory arthritis to improve referrals using multimodal machine learning from blood testing, semi-structured and unstructured patient records
Multimodal Machine Learning in Mental Health: A Survey of Data, Algorithms, and Challenges
Queen Mary University of London United Kingdom；Queen Mary University of London \& Jo z ef Stefan Institute United Kingdom \& Slovenia；Queen Mary University of London；Queen Mary University of London \& Jo z ef Stefan Institute
Machine learning-based multimodal prognostic models integrating pathology images and high-throughput omic data for overall survival prediction in cancer: a systematic review
Leeds Teaching Hospitals NHS Trust（利兹教学医院国民医疗服务信托）；University of Leeds（利兹大学）；Linköping University（林雪平大学）
Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Ochsner Health System（奥克斯纳医疗系统）；Leiden University Medical Center（莱顿大学医学中心）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Dartmouth College（达特茅斯学院）
Machine Unlearning in Hyperbolic vs. Euclidean Multimodal Contrastive Learning: Adapting Alignment Calibration to MERU
Aalborg University（奥尔堡大学）；University of Barcelona（巴塞罗那大学）；Computer Vision Center（计算机视觉中心）；Milestone Systems（里程碑系统公司）；Pioneer Center for Artificial Intelligence（先锋人工智能中心）
Forget-MI: Machine Unlearning for Forgetting Multimodal Information in Healthcare Settings
Department of Machine Learning, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Department of Computer Vision, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE Department of Computer Science, Michigan State University, United States 0.2cm
