Partially supervised segmentation is a label-saving method based on datasets with fractional classes labeled and intersectant. However, it is still far from landing on real-world medical applications due to privacy concerns and data heterogeneity. As a remedy without privacy leakage, federated partially supervised segmentation (FPSS) is formulated in this work. The main challenges for FPSS are class heterogeneity and client drift. We propose a Unified Federated Partially-labeled Segmentation (UFPS) framework to segment pixels within all classes for partially-annotated datasets by training a totipotential global model without class collision. Our framework includes Unified Label Learning and sparsed Unified Sharpness Aware Minimization for unification of class and feature space, respectively. We find that vanilla combinations for traditional methods in partially supervised segmentation and federated learning are mainly hampered by class collision through empirical study. Our comprehensive experiments on real medical datasets demonstrate better deconflicting and generalization ability of UFPS compared with modified methods.
Cross-device federated learning is an emerging machine learning (ML) paradigm where a large population of devices collectively train an ML model while the data remains on the devices. This research field has a unique set of practical challenges, and to systematically make advances, new datasets curated to be compatible with this paradigm are needed. Existing federated learning benchmarks in the image domain do not accurately capture the scale and heterogeneity of many real-world use cases. We introduce FLAIR, a challenging large-scale annotated image dataset for multi-label classification suit…
ForgeVLA: Federated Vision-Language-Action Learning without Language Annotations
Sichuan University（四川大学）；Zhejiang University（浙江大学）；Ludwig-Maximilians-Universität München（慕尼黑路德维希-马克西米利安大学）；Lenovo Group Limited（联想集团有限公司）；Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education（教育部机器学习与工业智能工程研究中心）
Technical University of Munich（慕尼黑工业大学）；Johns Hopkins University（约翰斯·霍普金斯大学）；University Hospital Bonn（波恩大学医院）；Helmholtz Zentrum München（亥姆霍兹慕尼黑中心）
Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and mortality rate substantially. The recent improvement in automated cancer classification using deep learning methods has reached a human-level performance requiring a large amount of annotated data assembled in one location, yet, finding such conditions usually is not feasible. Recently, federated learning (FL) has been proposed to train decentralized models in a privacy-preserved fashion depending on labeled data at…
From Isolation to Collaboration: Federated Class-Heterogeneous Learning for Chest X-Ray Classification
University of Maryland School of Medicine（马里兰大学医学院）；University of Maryland Institute for Health Computing（马里兰大学健康计算研究所）；St. Jude Children’s Research Hospital（圣犹大儿童研究医院）
The Hong Kong University of Science and Technology（香港科技大学）；Huazhong University of Science and Technology（华中科技大学）
The purpose of federated learning is to enable multiple clients to jointly train a machine learning model without sharing data. However, the existing methods for training an image segmentation model have been based on an unrealistic assumption that the training set for each local client is annotated in a similar fashion and thus follows the same image supervision level. To relax this assumption, in this work, we propose a label-agnostic unified federated learning framework, named FedMix, for medical image segmentation based on mixed image labels. In FedMix, each client updates the federated mo…
WeBank Co. Ltd.（微众银行）；Hong Kong University of Science and Technology（香港科技大学）；Extreme Vision Co. Ltd.（极视角科技有限公司）；South China University of Technology（华南理工大学）；Shenzhen University（深圳大学）
Federated learning is a new machine learning paradigm which allows data parties to build machine learning models collaboratively while keeping their data secure and private. While research efforts on federated learning have been growing tremendously in the past two years, most existing works still depend on pre-existing public datasets and artificial partitions to simulate data federations due to the lack of high-quality labeled data generated from real-world edge applications. Consequently, advances on benchmark and model evaluations for federated learning have been lagging behind. In this pa…
Warsaw University of Technology（华沙理工大学）；Systems Research Institute Polish Academy of Sciences（波兰科学院系统研究所）；Warsaw Management University（华沙管理大学）
Availability of large amount of annotated data is one of the pillars of deep learning success. Although numerous big datasets have been made available for research, this is often not the case in real life applications (e.g. companies are not able to share data due to GDPR or concerns related to intellectual property rights protection). Federated learning (FL) is a potential solution to this problem, as it enables training a global model on data scattered across multiple nodes, without sharing local data itself. However, even FL methods pose a threat to data privacy, if not handled properly. Th…
Federated learning is a paradigm that enables local devices to jointly train a server model while keeping the data decentralized and private. In federated learning, since local data are collected by clients, it is hardly guaranteed that the data are correctly annotated. Although a lot of studies have been conducted to train the networks robust to these noisy data in a centralized setting, these algorithms still suffer from noisy labels in federated learning. Compared to the centralized setting, clients' data can have different noise distributions due to variations in their labeling systems or …
Johns Hopkins University（约翰斯·霍普金斯大学）；University of Maryland Medical Intelligent Imaging (UM2ii) Center（马里兰大学医学智能成像（UM2ii）中心）
Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications. However, generating manual annotations for medical images is time-consuming, requires high skill, and is an expensive effort, especially for 3D images. One potential solution is to aggregate knowledge from partially annotated datasets from multiple groups to collaboratively train global models using Federated Learning. To this end, we propose SegViz, a federated learning-based framework to train a segmentation model from distributed non-i.i.d datasets with…
Beijing UCAS Space Technology Co., Ltd.（北京国科大空间技术有限公司）；University of Science and Technology of China（中国科学技术大学）；Institute of Computing Technology, CAS（中国科学院计算技术研究所）
Objective and Impact Statement: Accurate organ segmentation is critical for many clinical applications at different clinical sites, which may have their specific application requirements that concern different organs. Introduction: However, learning high-quality, site-specific organ segmentation models is challenging as it often needs on-site curation of a large number of annotated images. Security concerns further complicate the matter. Methods: The paper aims to tackle these challenges via a two-phase aggregation-then-adaptation approach. The first phase of federated aggregation learns a sin…
Benchmarking Federated Learning for Semantic Datasets: Federated Scene Graph Generation
Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology（人工智能研究生院，乌山国立科学技术研究所）；Department of Electrical Engineering, Ulsan National Institute of Science and Technology（电气工程系，乌山国立科学技术研究所）；Electronics and Telecommunications Research Institute (ETRI)（电子电信研究院（ETRI））
Bridging Data Islands: Geographic Heterogeneity-Aware Federated Learning for Collaborative Remote Sensing Semantic Segmentation
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；Space Research Institute, Russian Academy of Sciences（俄罗斯科学院空间研究所）；College of Engineering, Purdue University（普渡大学工程学院）；Institute of Surface-Earth System Science, School of Earth System Science, Tianjin University（天津大学地球系统科学学院地表-地球系统科学研究所）
University of Oxford（牛津大学）；UiT The Arctic University of Norway（挪威北极大学）
Using decentralized data for federated training is one promising emerging research direction for alleviating data scarcity in the medical domain. However, in contrast to large-scale fully labeled data commonly seen in general object recognition tasks, the local medical datasets are more likely to only have images annotated for a subset of classes of interest due to high annotation costs. In this paper, we consider a practical yet under-explored problem, where underrepresented classes only have few labeled instances available and only exist in a few clients of the federated system. We show that…
Federated Self-Supervised Learning for One-Shot Cross-Modal and Cross-Imaging Technique Segmentation
Hong Kong Baptist University（香港浸会大学）；Narayana Superspeciality Hospital（纳拉亚纳超级专科医院）；National Institute of Technology Durgapur（杜尔加布尔国家理工学院）；Indian Institute of Technology（印度理工学院）
Federated Learning with Partially Labeled Data: A Conditional Distillation Approach
Department of Computer Science and Information Engineering, National Taiwan University（计算机科学与信息工程系，国立台湾大学）；Graduate School of Informatics, Nagoya University（资讯研究生院，名古屋大学）；Institute of Applied Mathematical Sciences, National Taiwan University（应用数学科学研究所，国立台湾大学）；NVIDIA Corporation（NVIDIA公司）
Helmholtz Zentrum München（慕尼黑亥姆霍兹中心）；Technical University of Munich（慕尼黑工业大学）；University of Freiburg（弗莱堡大学）；University of Bonn（波恩大学）
Given the high incidence and effective treatment options for liver diseases, they are of great socioeconomic importance. One of the most common methods for analyzing CT and MRI images for diagnosis and follow-up treatment is liver segmentation. Recent advances in deep learning have demonstrated encouraging results for automatic liver segmentation. Despite this, their success depends primarily on the availability of an annotated database, which is often not available because of privacy concerns. Federated Learning has been recently proposed as a solution to alleviate these challenges by trainin…
FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification
South China University of Technology（华南理工大学）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；The Chinese University of Hong Kong（香港中文大学）；Tianjin Medical University Cancer Institute and Hospital（天津医科大学肿瘤医院）
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）
Brigham and Women’s Hospital（布里格姆妇女医院）；Harvard Medical School（哈佛医学院）；Broad Institute of Harvard and MIT（哈佛-麻省理工学院布罗德研究所）；Department of Pathology（病理系）；Northwell Health（诺斯韦尔健康）；Department of Biomedical Informatics（生物医学信息学系）；Dana-Farber Cancer Institute（达纳-法伯癌症研究所）；Cancer Program（癌症项目）；Cancer Data Science（癌症数据科学）
Deep Learning-based computational pathology algorithms have demonstrated profound ability to excel in a wide array of tasks that range from characterization of well known morphological phenotypes to predicting non-human-identifiable features from histology such as molecular alterations. However, the development of robust, adaptable, and accurate deep learning-based models often rely on the collection and time-costly curation large high-quality annotated training data that should ideally come from diverse sources and patient populations to cater for the heterogeneity that exists in such dataset…
University of Maryland School of Medicine（马里兰大学医学院）；Johns Hopkins University（约翰斯·霍普金斯大学）
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease. In real-world clinical use, multiple diseases need to be considered since they can co-exist in the same patient. In this work, we demonstrate how federated learning (FL) can be used to make these toy CXR datasets from Kaggle clinically useful. Specifically, we train a single FL classification model (`global`) using two separate CXR datasets -- one annotated for presence of pneumonia and the …
Robust Federated Learning against Noisy Clients via Masked Optimization
Institute of Computing Technology, Chinese Academy of Sciences & University of Chinese Academy of Sciences（计算技术研究所，中国科学院及中国科学院大学）；Department of Electronic Engineering, The Chinese University of Hong Kong（电子工程系，香港中文大学）；Institute of Computing Technology, Chinese Academy of Sciences（计算技术研究所，中国科学院）
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Ecole de Technologie Superieure（高等技术学院）；Taibah University（泰巴大学）；University of Lorraine（洛林大学）；University of Sharjah（沙迦大学）；Centre Hospitalier Universitaire Vaudois（沃州大学医院中心）
Real World Federated Learning with a Knowledge Distilled Transformer for Cardiac CT Imaging
DZHK (German Centre for Cardiovascular Research), all partner sites Department of Cardiology, Angiology；Pneumology, Heidelberg University Hospital, Heidelberg, Germany Informatics for Life Institute, Heidelberg, Germany Clinic for Cardiology III, University Hospital M\"unster, M\"unster, Germany Department of Medicine I, LMU University Hospital, LMU Munich, Munich, Germany Department of Diagnostic；Nuclear Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany Department of Medical Statistics, University Medical Center G\"ottingen, G\"ottingen, Germany Deutsches Herzzentrum der Charité (DHZC), Institute of Computer-assisted Cardiovascular Medicine, Berlin, Germany Charité – Universit\"atsmedizin Berlin, corporate member of Freie Universit\"at Berlin；Humboldt-Universit\"at zu Berlin, Berlin, Germany Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany Institute of Bioinformatics, University Medicine Greifswald, Greifswald, Germany Institute for Experimental；Translational Cardiovascular Imaging, Goethe University, Frankfurt am Main, Germany Department of Cardiology, University Heart；Vascular Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany Department of Cardiology, University Medicine G\"ottingen, G\"ottingen, Germany Department of Cardiology, Campus Kerckhoff of the Justus-Liebig-University at Gießen, Kerckhoff-Clinic, Gießen, Germany
