mlsanityData to 7 Oct 2026

Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

arXiv 2108.08537 ↗First posted 19 Aug 2021cs.CVcited by 19

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Abstract (arXiv)

Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with "healthy'' pancreases only while datasets from other clients may contain cases with pancreatic tumors. The vanilla federated averaging algorithm makes it possible to obtain more generalizable deep learning-based segmentation models representing the training data from multiple institutions without centralizing datasets. However, it might be sub-optimal for the aforementioned multi-task scenarios. In this paper, we investigate heterogeneous optimization methods that show improvements for the automated segmentation of pancreas and pancreatic tumors in abdominal CT images with FL settings.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 14 of its 21 references that are in the map. First posted on arXiv 19 Aug 2021.

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14 of 21 references.

References · 14

  1. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,592
  2. 2018Federated Optimization in Heterogeneous Networkscited by 2,971
  3. 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
  4. 2017Federated Multi-Task Learningcited by 669
  5. 2018End-to-End Multi-Task Learning with Attentioncited by 533
  6. 2019A large annotated medical image dataset for the development and evaluation of segmentation algorithmscited by 371
  7. 2019Privacy-preserving Federated Brain Tumour Segmentationcited by 169
  8. 2015DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentationcited by 158
  9. 2018Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentationcited by 148
  10. 2019C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentationcited by 49
  11. 2020Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japancited by 23
  12. 2021Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentationcited by 19
2 more
  1. 2019Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Netcited by 15
  2. 2020Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learningcited by 11

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Citing papers per year: 2021: 1; 2022: 5; 2023: 6; 2024: 6; 2025: 1.

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  1. 2022Federated Learning Enables Big Data for Rare Cancer Boundary Detectioncited by 83
  2. 2022Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directionscited by 34
  3. 2023Recent Methodological Advances in Federated Learning for Healthcarecited by 11
  4. 2024FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injectioncited by 8
  5. 2021Cross-Domain Federated Learning in Medical Imagingcited by 6
  6. 2024Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalitiescited by 5
  7. 2024Navigating Distribution Shifts in Medical Image Analysis: A Surveycited by 5
  8. 2022Federated Learning for the Classification of Tumor Infiltrating Lymphocytescited by 4
  9. 2022Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priorscited by 4
  10. 2023Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregationcited by 4
  11. 2022MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learningcited by 3
  12. 2024Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgerycited by 2
7 more
  1. 2024Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Modelscited by 1
  2. 2025Federated learning, ethics, and the double black box problem in medical AIcited by 1
  3. 2023SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotationscited by 0
  4. 2023Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentationcited by 0
  5. 2023SoK: Assessing the State of Applied Federated Machine Learningcited by 0
  6. 2023Tackling Heterogeneity in Medical Federated learning via Vision Transformerscited by 0
  7. 2024MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentationcited by 0