Federated Simulation for Medical Imaging

Li, Daiqing; Kar, Amlan; Ravikumar, Nishant; Frangi, Alejandro F.; Fidler, Sanja

doi:10.1007/978-3-030-59710-8_16

Cited by 32 publications

(19 citation statements)

References 12 publications

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“…Representatively, McMahan et al [36] propose the popular federated averaging algorithm for communication-efficient federated training of deep networks. With the advantage of privacy protection, FL has recently drawn increasing interests in medical image applications [4,18,22,27,45,49,51]. Sheller et al [49] is a pilot study to investigate the collaborative model training without sharing patient data for the multi-site brain tumor segmentation.…”

Section: Federated Learning In Medical Imagingmentioning

confidence: 99%

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Liu

Chen

Qin

et al. 2021

Preprint

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Federated learning allows distributed medical institutions to collaboratively learn a shared prediction model with privacy protection. While at clinical deployment, the models trained in federated learning can still suffer from performance drop when applied to completely unseen hospitals outside the federation. In this paper, we point out and solve a novel problem setting of federated domain generalization (FedDG), which aims to learn a federated model from multiple distributed source domains such that it can directly generalize to unseen target domains. We present a novel approach, named as Episodic Learning in Continuous Frequency Space (ELCFS), for this problem by enabling each client to exploit multi-source data distributions under the challenging constraint of data decentralization. Our approach transmits the distribution information across clients in a privacy-protecting way through an effective continuous frequency space interpolation mechanism. With the transferred multi-source distributions, we further carefully design a boundary-oriented episodic learning paradigm to expose the local learning to domain distribution shifts and particularly meet the challenges of model generalization in medical image segmentation scenario. The effectiveness of our method is demonstrated with superior performance over state-of-the-arts and in-depth ablation experiments on two medical image segmentation tasks. The code is available at https://github.com/liuquande/FedDG-ELCFS.

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Section: Federated Learning In Medical Imagingmentioning

confidence: 99%

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Liu

Chen

Qin

et al. 2021

Preprint

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“…Sharing frequency domain information enables the separation of semantic information from noise in the original images. Li et al 18 tackles the problem of domain adaptation with a physics-driven generative approach to disentangle the information about model and geometry from the imaging sensor.…”

Section: Introductionmentioning

confidence: 99%

Federated Learning and Differential Privacy for Medical Image Analysis

Adnan

Kalra

Cresswell³

et al. 2021

Preprint

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The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We also study the effect of different source domains for histopathology images by evaluating the performance using external validation. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.

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“…Recently, federated learning (FL) has emerged as a privacy-preserving solution for this, which allows to learn from distributed data sources by aggregating the locally learned model parameters without exchanging the sensitive health data [8,12,14,16,23]. However, despite progress achieved, existing FL algorithms typically only allow the supervised training setting [3,13,15,24,26], which has limited the local clients (i.e., hospitals) without data annotations to join the FL process. Yet, in realistic scenarios, most hospitals usually cannot afford the intricate data labeling due to lack of budget or expertise [22].…”

Section: Introductionmentioning

confidence: 99%

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

Liu

Yang

Dou

et al. 2021

Lecture Notes in Computer Science

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Federated learning (FL) has emerged with increasing popularity to collaborate distributed medical institutions for training deep networks. However, despite existing FL algorithms only allow the supervised training setting, most hospitals in realistic usually cannot afford the intricate data labeling due to absence of budget or expertise. This paper studies a practical yet challenging FL problem, named Federated Semi-supervised Learning (FSSL), which aims to learn a federated model by jointly utilizing the data from both labeled and unlabeled clients (i.e., hospitals). We present a novel approach for this problem, which improves over traditional consistency regularization mechanism with a new inter-client relation matching scheme. The proposed learning scheme explicitly connects the learning across labeled and unlabeled clients by aligning their extracted disease relationships, thereby mitigating the deficiency of task knowledge at unlabeled clients and promoting discriminative information from unlabeled samples. We validate our method on two large-scale medical image classification datasets. The effectiveness of our method has been demonstrated with the clear improvements over state-of-the-arts as well as the thorough ablation analysis on both tasks 4 .

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Federated Simulation for Medical Imaging

Cited by 32 publications

References 12 publications

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space

Federated Learning and Differential Privacy for Medical Image Analysis

Federated Semi-supervised Medical Image Classification via Inter-client Relation Matching

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