Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation

• URL: http://arxiv.org/abs/2302.06352v2
• Date: Tue, 14 Feb 2023 09:06:03 GMT
• Title: Deep Anatomical Federated Network (Dafne): an open client/server framework for the continuous collaborative improvement of deep-learning-based medical image segmentation
• Authors: Francesco Santini, Jakob Wasserthal, Abramo Agosti, Xeni Deligianni, Kevin R. Keene, Hermien E. Kan, Stefan Sommer, Christoph Stuprich, Fengdan Wang, Claudia Weidensteiner, Giulia Manco, Matteo Paoletti, Valentina Mazzoli, Arjun Desai, and Anna Pichiecchio
• Abstract summary: The Dafne solution implements continuously evolving deep learning models exploiting the collective knowledge of the users of the system. Dafne is the first decentralized, collaborative solution that implements continuously evolving deep learning models exploiting the collective knowledge of the users of the system. The models deployed through Dafne are able to improve their performance over time and to generalize to data types not seen in the training sets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Semantic segmentation is a crucial step to extract quantitative information from medical (and, specifically, radiological) images to aid the diagnostic process, clinical follow-up. and to generate biomarkers for clinical research. In recent years, machine learning algorithms have become the primary tool for this task. However, its real-world performance is heavily reliant on the comprehensiveness of training data. Dafne is the first decentralized, collaborative solution that implements continuously evolving deep learning models exploiting the collective knowledge of the users of the system. In the Dafne workflow, the result of each automated segmentation is refined by the user through an integrated interface, so that the new information is used to continuously expand the training pool via federated incremental learning. The models deployed through Dafne are able to improve their performance over time and to generalize to data types not seen in the training sets, thus becoming a viable and practical solution for real-life medical segmentation tasks.

Related papers

• Coupling AI and Citizen Science in Creation of Enhanced Training Dataset for Medical Image Segmentation [3.7274206780843477]
  We introduce a robust and versatile framework that combines AI and crowdsourcing to improve the quality and quantity of medical image datasets. Our approach utilise a user-friendly online platform that enables a diverse group of crowd annotators to label medical images efficiently. We employ pix2pixGAN, a generative AI model, to expand the training dataset with synthetic images that capture realistic morphological features.
  arXiv  Detail & Related papers  (2024-09-04T21:22:54Z)
• A Classifier-Free Incremental Learning Framework for Scalable Medical Image Segmentation [6.591403935303867]
  We introduce a novel segmentation paradigm enabling the segmentation of a variable number of classes within a single classifier-free network. This network is trained using contrastive learning and produces discriminative feature representations that facilitate straightforward interpretation. We demonstrate the flexibility of our method in handling varying class numbers within a unified network and its capacity for incremental learning.
  arXiv  Detail & Related papers  (2024-05-25T19:05:07Z)
• 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)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions [66.40971096248946]
  In this paper, we collect a series of MedISeg tricks for different model implementation phases. We experimentally explore the effectiveness of these tricks on consistent baselines. We also open-sourced a strong MedISeg repository, where each component has the advantage of plug-and-play.
  arXiv  Detail & Related papers  (2022-09-21T12:30:05Z)
• LifeLonger: A Benchmark for Continual Disease Classification [59.13735398630546]
  We introduce LifeLonger, a benchmark for continual disease classification on the MedMNIST collection. Task and class incremental learning of diseases address the issue of classifying new samples without re-training the models from scratch. Cross-domain incremental learning addresses the issue of dealing with datasets originating from different institutions while retaining the previously obtained knowledge.
  arXiv  Detail & Related papers  (2022-04-12T12:25:05Z)
• Federated Cycling (FedCy): Semi-supervised Federated Learning of Surgical Phases [57.90226879210227]
  FedCy is a semi-supervised learning (FSSL) method that combines FL and self-supervised learning to exploit a decentralized dataset of both labeled and unlabeled videos. We demonstrate significant performance gains over state-of-the-art FSSL methods on the task of automatic recognition of surgical phases.
  arXiv  Detail & Related papers  (2022-03-14T17:44:53Z)
• BERT WEAVER: Using WEight AVERaging to enable lifelong learning for transformer-based models in biomedical semantic search engines [49.75878234192369]
  We present WEAVER, a simple, yet efficient post-processing method that infuses old knowledge into the new model. We show that applying WEAVER in a sequential manner results in similar word embedding distributions as doing a combined training on all data at once.
  arXiv  Detail & Related papers  (2022-02-21T10:34:41Z)
• PMFL: Partial Meta-Federated Learning for heterogeneous tasks and its applications on real-world medical records [11.252157002705484]
  Federated machine learning is a versatile and flexible tool to utilize distributed data from different sources. We propose a new algorithm, which is an integration of federated learning and meta-learning, to tackle this issue. We show that our algorithm could obtain the fastest training speed and achieve the best performance when dealing with heterogeneous medical datasets.
  arXiv  Detail & Related papers  (2021-12-10T03:55:03Z)
• Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation [57.78765460295249]
  We develop a novel automatic learning-based data augmentation method for medical image segmentation. In our method, we innovatively combine the data augmentation module and the subsequent segmentation module in an end-to-end training manner with a consistent loss. We extensively evaluated our method on CT kidney tumor segmentation which validated the promising results of our method.
  arXiv  Detail & Related papers  (2020-02-22T14:10:13Z)

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.