A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation

• URL: http://arxiv.org/abs/2306.08720v1
• Date: Wed, 14 Jun 2023 19:49:47 GMT
• Title: A Client-server Deep Federated Learning for Cross-domain Surgical Image Segmentation
• Authors: Ronast Subedi, Rebati Raman Gaire, Sharib Ali, Anh Nguyen, Danail Stoyanov, and Binod Bhattarai
• Abstract summary: This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. We propose a Client-server deep federated architecture for cross-domain adaptation.
• Score: 18.402074964118697
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper presents a solution to the cross-domain adaptation problem for 2D surgical image segmentation, explicitly considering the privacy protection of distributed datasets belonging to different centers. Deep learning architectures in medical image analysis necessitate extensive training data for better generalization. However, obtaining sufficient diagnostic and surgical data is still challenging, mainly due to the inherent cost of data curation and the need of experts for data annotation. Moreover, increased privacy and legal compliance concerns can make data sharing across clinical sites or regions difficult. Another ubiquitous challenge the medical datasets face is inevitable domain shifts among the collected data at the different centers. To this end, we propose a Client-server deep federated architecture for cross-domain adaptation. A server hosts a set of immutable parameters common to both the source and target domains. The clients consist of the respective domain-specific parameters and make requests to the server while learning their parameters and inferencing. We evaluate our framework in two benchmark datasets, demonstrating applicability in computer-assisted interventions for endoscopic polyp segmentation and diagnostic skin lesion detection and analysis. Our extensive quantitative and qualitative experiments demonstrate the superiority of the proposed method compared to competitive baseline and state-of-the-art methods. Codes are available at: https://github.com/thetna/distributed-da

Related papers

• Federated Semi-supervised Learning for Medical Image Segmentation with intra-client and inter-client Consistency [10.16245019262119]
  Federated learning aims to train a shared model of isolated clients without local data exchange. In this work, we propose a novel federated semi-supervised learning framework for medical image segmentation.
  arXiv  Detail & Related papers  (2024-03-19T12:52:38Z)
• Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals [9.845637899896365]
  Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. We propose a novel Sourece-Free Active Domain Adaptation (SFADA) framework to facilitate domain adaptation for the Gross Tumor Volume (GTV) segmentation task. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach.
  arXiv  Detail & Related papers  (2023-09-23T15:26:27Z)
• Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning [57.43322536718131]
  We present a two-stage source-free domain adaptation (SFDA) framework for medical image segmentation. In the prototype-anchored feature alignment stage, we first utilize the weights of the pre-trained pixel-wise classifier as source prototypes. Then, we introduce the bi-directional transport to align the target features with class prototypes by minimizing its expected cost.
  arXiv  Detail & Related papers  (2023-07-19T06:07:12Z)
• Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation [27.49427483473792]
  In real-world scenarios, it is common for models to encounter data from new and different domains to which they were not exposed to during training. domain generalization (DG) is a promising direction as it enables models to handle data from previously unseen domains. We introduce a novel DG method called Adversarial Intensity Attack (AdverIN), which leverages adversarial training to generate training data with an infinite number of styles.
  arXiv  Detail & Related papers  (2023-04-05T19:40:51Z)
• AADG: Automatic Augmentation for Domain Generalization on Retinal Image Segmentation [1.0452185327816181]
  We propose a data manipulation based domain generalization method, called Automated Augmentation for Domain Generalization (AADG) Our AADG framework can effectively sample data augmentation policies that generate novel domains. Our proposed AADG exhibits state-of-the-art generalization performance and outperforms existing approaches.
  arXiv  Detail & Related papers  (2022-07-27T02:26:01Z)
• Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary [64.5632303184502]
  Domain generalization typically requires data from multiple source domains for model learning. This paper studies the important yet challenging single domain generalization problem, in which a model is learned under the worst-case scenario with only one source domain to directly generalize to different unseen target domains. We present a novel approach to address this problem in medical image segmentation, which extracts and integrates the semantic shape prior information of segmentation that are invariant across domains.
  arXiv  Detail & Related papers  (2022-06-29T08:46:27Z)
• Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography [0.09490124006642771]
  We investigated the implementation of a Cycle-Consistent Generative Adrative Networks (CycleGAN) for the domain adaptation of Optical Coherence Tomography ( OCT) volumes. This study was done in collaboration with the Biomedical Optics Research Group and Functional & Anatomical Imaging & Shape Analysis Lab at Simon Fraser University.
  arXiv  Detail & Related papers  (2021-07-06T02:07:53Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization [59.5104563755095]
  We introduce a simple but effective approach to improve the generalization capability of deep neural networks in the field of medical imaging classification. Motivated by the observation that the domain variability of the medical images is to some extent compact, we propose to learn a representative feature space through variational encoding.
  arXiv  Detail & Related papers  (2020-09-27T12:30:30Z)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)
• VerSe: A Vertebrae Labelling and Segmentation Benchmark for Multi-detector CT Images [121.31355003451152]
  Large Scale Vertebrae Challenge (VerSe) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020. We present the the results of this evaluation and further investigate the performance-variation at vertebra-level, scan-level, and at different fields-of-view.
  arXiv  Detail & Related papers  (2020-01-24T21:09:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.