The computer-aided disease diagnosis from radiomic data is important in many medical applications. However, developing such a technique relies on annotating radiological images, which is a time-consuming, labor-intensive, and expensive process. In this work, we present the first novel collaborative self-supervised learning method to solve the challenge of insufficient labeled radiomic data, whose characteristics are different from text and image data. To achieve this, we present two collaborative pretext tasks that explore the latent pathological or biological relationships between regions of interest and the similarity and dissimilarity information between subjects. Our method collaboratively learns the robust latent feature representations from radiomic data in a self-supervised manner to reduce human annotation efforts, which benefits the disease diagnosis. We compared our proposed method with other state-of-the-art self-supervised learning methods on a simulation study and two independent datasets. Extensive experimental results demonstrated that our method outperforms other self-supervised learning methods on both classification and regression tasks. With further refinement, our method shows the potential advantage in automatic disease diagnosis with large-scale unlabeled data available.
Imbalance-Aware Self-Supervised Learning for 3D Radiomic Representations
Technical University of Munich（慕尼黑工业大学）；University of Zurich（苏黎世大学）；Southern University of Science and Technology（南方科技大学）；ETH Zurich（苏黎世联邦理工学院）；Macau University of Science and Technology（澳门科技大学）；Klinikum rechts der Isar（伊萨尔河右岸医院）
A review of handcrafted and deep radiomics in neurological diseases: transitioning from oncology to clinical neuroimaging
Maastricht University（马斯特里赫特大学）；University of Liege（列日大学）；Maastricht University Medical Centre（马斯特里赫特大学医学中心）；University of Hasselt（哈塞尔特大学）；CHU Liège（列日大学医院中心）；GROW—School for Oncology（GROW肿瘤学院）；Biomedical Research Institute (BIOMED)（生物医学研究所（BIOMED））；GIGA Cyclotrone Research Center（GIGA回旋加速器研究中心）；GIGA In Silico Medicine（GIGA计算机模拟医学中心）；Department of Precision Medicine（精准医学系）；Department of Radiology and Nuclear Medicine（放射科与核医学科）；Neurology Department（神经内科）
University of Hong Kong（香港大学）；South China University of Technology（华南理工大学）；Wilfrid Laurier University（威尔弗里德·劳雷尔大学）；Baidu Inc.（百度公司）；York University（约克大学）
Hong Kong Baptist University（香港浸会大学）；CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences（奥地利科学院分子医学CeMM研究中心）；Universität Vienna（维也纳大学）；Shenzhen University（深圳大学）
Lifelong Evolution: Collaborative Learning between Large and Small Language Models for Continuous Emergent Fake News Detection
School of Cyber Science and Technology, Beihang University（信息科学与技术学院，北京航空航天大学）；School of Cyber Science and Technology, Beijing University of Posts and Telecommunications（信息科学与技术学院，北京邮电大学）；Department of Computer Science, University of Illinois at Chicago（计算机科学系，伊利诺伊大学芝加哥分校）
SelfCoLearn: Self-supervised collaborative learning for accelerating dynamic MR imaging
Xiangtan University（湘潭大学）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Pengcheng Laboratory（鹏城实验室）；Jinan University（暨南大学）
EMeRALDS: Electronic Medical Record Driven Automated Lung Nodule Detection and Classification in Thoracic CT Images
Faculty of Electrical and Electronics Engineering, University of Engineering（电气电子工程学院，工程大学）；Department of Engineering Sciences, University of Agder（工程科学系，阿格德大学）；Sheikh Zayed Institute for Pediatric Surgical Innovation, Children’s National Hospital（谢赫扎耶德小儿外科创新研究所，儿童医院）；School of Medicine and Health Sciences, George Washington University（医学与健康科学学院，乔治华盛顿大学）
Dealing With Heterogeneous 3D MR Knee Images: A Federated Few-Shot Learning Method With Dual Knowledge Distillation
Rutgers University（罗格斯大学）；Sichuan University West China Hospital（四川大学华西医院）；Walmart Global Tech（沃尔玛全球科技）；Shanghai Jiao Tong University School of Medicine（上海交通大学医学院）；Tongren Hospital（同仁医院）；Fudan University（复旦大学）；Zhongshan Hospital（中山医院）；SenseTime Research（商汤科技研究院）
Unsupervised Domain Adaptation for Cross-Modality Retinal Vessel Segmentation via Disentangling Representation Style Transfer and Collaborative Consistency Learning
Southern University of Science and Technology（南方科技大学）；The University of Hong Kong（香港大学）
