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

Liu, Quande; Yang, Hongzheng; Dou, Qi; Heng, Pheng‐Ann

doi:10.1007/978-3-030-87199-4_31

Cited by 52 publications

(23 citation statements)

References 28 publications

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“…[36] proposed an adaptive layer-wise parameter selection method for uploading models for aggregation. Other works that also achieve competitive performance on benchmark datasets include [37]- [39]. [37] adapted a combination of two stateof-the-art semi-supervised methods, FixMatch [17] and Mix-Match [19], in a federated setting.…”

Section: B Federated Learning With Unlabeled Datamentioning

confidence: 99%

“…[37] adapted a combination of two stateof-the-art semi-supervised methods, FixMatch [17] and Mix-Match [19], in a federated setting. [38] and [39] proposed methods based on contrastive learning and knowledge distillation, respectively. Apart from classification, FSSL has been also used for the task of COVID-19 region segmentation in chest computed tomography scans [40].…”

Section: B Federated Learning With Unlabeled Datamentioning

confidence: 99%

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Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Kassem¹,

Alapatt²,

Mascagni³

et al. 2022

Preprint

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Recent advancements in deep learning methods bring computer-assistance a step closer to fulfilling promises of safer surgical procedures. However, the generalizability of such methods is often dependent on training on diverse datasets from multiple medical institutions, which is a restrictive requirement considering the sensitive nature of medical data. Recently proposed collaborative learning methods such as Federated Learning (FL) allow for training on remote datasets without the need to explicitly share data. Even so, data annotation still represents a bottleneck, particularly in medicine and surgery where clinical expertise is often required. With these constraints in mind, we propose FedCy, a federated semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos, thereby improving performance on the task of surgical phase recognition. By leveraging temporal patterns in the labeled data, FedCy helps guide unsupervised training on unlabeled data towards learning taskspecific features for phase recognition. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases using a newly collected multi-institutional dataset of laparoscopic cholecystectomy videos. Furthermore, we demonstrate that our approach also learns more generalizable features when tested on data from an unseen domain.

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Section: B Federated Learning With Unlabeled Datamentioning

confidence: 99%

Section: B Federated Learning With Unlabeled Datamentioning

confidence: 99%

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Kassem¹,

Alapatt²,

Mascagni³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…In a standard federated learning setting, not every local client has access to pixel-level supervision for image segmentation to facilitate model learning with weakly-labeled and unlabeled training data. To this end, some semi-supervised federated learning approaches require clients to share supplementary information, e.g., client-specific disease relationship [32], extracted features from raw data [34], metadata of the training data [35], and ensemble predictions from different clients' locally-updated models besides their parameters [33]. Additional information sharing beyond the locally-updated model parameters may leak privacy-sensitive information [45] about clients' data.…”

Section: Semi-supervised Federated Learningmentioning

confidence: 99%

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

Wicaksana¹,

Yan²,

Zhang³

et al. 2022

Preprint

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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 model by integrating and effectively making use of all available labeled data ranging from strong pixel-level labels, weak bounding box labels, to weakest image-level class labels. Based on these local models, we further propose an adaptive weight assignment procedure across local clients, where each client learns an aggregation weight during the global model update. Compared to the existing methods, FedMix not only breaks through the constraint of a single level of image supervision, but also can dynamically adjust the aggregation weight of each local client, achieving rich yet discriminative feature representations. To evaluate its effectiveness, experiments have been carried out on two challenging medical image segmentation tasks, i.e., breast tumor segmentation and skin lesion segmentation. The results validate that our proposed FedMix outperforms the state-of-the-art methods by a large margin 1 .

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“…In detail, the authors used two different data augmentation methods to transform the unlabeled data, and supervised the strongly augmented samples with sharpened low-entropy prediction on the weakly augmented samples. Since the semi-supervised learning problem also exists in the FL scenario, some current studies [13], [32] and [33] investigated to construct SSL framework for FL based applications. As an instance, Jeong et al in [13] presented an inter-client consistency loss and a disjoint learning pattern on labeled and unlabeled data.…”

Section: Related Workmentioning

confidence: 99%

Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

Shi¹,

Chen²,

Zhang³

2022

Preprint

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Since federated learning (FL) has been introduced as a decentralized learning technique with privacy preservation, statistical heterogeneity of distributed data stays the main obstacle to achieve robust performance and stable convergence in FL applications. Model personalization methods have been studied to overcome this problem. However, existing approaches are mainly under the prerequisite of fully labeled data, which is unrealistic in practice due to the requirement of expertise. The primary issue caused by partial-labeled condition is that, clients with deficient labeled data can suffer from unfair performance gain because they lack adequate insights of local distribution to customize the global model. To tackle this problem, 1) we propose a novel personalized semi-supervised learning paradigm which allows partial-labeled or unlabeled clients to seek labeling assistance from data-related clients (helper agents), thus to enhance their perception of local data; 2) based on this paradigm, we design an uncertainty-based data-relation metric to ensure that selected helpers can provide trustworthy pseudo labels instead of misleading the local training; 3) to mitigate the network overload introduced by helper searching, we further develop a helper selection protocol to achieve efficient communication with negligible performance sacrifice. Experiments show that our proposed method can obtain superior performance and more stable convergence than other related works with partial labeled data, especially in highly heterogeneous setting.

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

Cited by 52 publications

References 28 publications

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

Uncertainty Minimization for Personalized Federated Semi-Supervised Learning

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