# Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

arXiv: https://arxiv.org/abs/2108.08537 · HTML: https://mlsanity.com/paper/2108.08537/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Federated Multi-Task Learning](https://mlsanity.com/paper/1705.10467/) (2017)
- [End-to-End Multi-Task Learning with Attention](https://mlsanity.com/paper/1803.10704/) (2018)
- [A large annotated medical image dataset for the development and evaluation of segmentation algorithms](https://mlsanity.com/paper/1902.09063/) (2019)
- [Privacy-preserving Federated Brain Tumour Segmentation](https://mlsanity.com/paper/1910.00962/) (2019)
- [DeepOrgan: Multi-level Deep Convolutional Networks for Automated Pancreas Segmentation](https://mlsanity.com/paper/1506.06448/) (2015)
- [Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation](https://mlsanity.com/paper/1810.04304/) (2018)
- [C2FNAS: Coarse-to-Fine Neural Architecture Search for 3D Medical Image Segmentation](https://mlsanity.com/paper/1912.09628/) (2019)
- [Federated Semi-Supervised Learning for COVID Region Segmentation in Chest CT using Multi-National Data from China, Italy, Japan](https://mlsanity.com/paper/2011.11750/) (2020)
- [Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation](https://mlsanity.com/paper/2104.10195/) (2021)
- [Deep Q Learning Driven CT Pancreas Segmentation with Geometry-Aware U-Net](https://mlsanity.com/paper/1904.09120/) (2019)
- [Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning](https://mlsanity.com/paper/2009.13148/) (2020)

## Built on by (most cited first, 19 of 19)

- [Federated Learning Enables Big Data for Rare Cancer Boundary Detection](https://mlsanity.com/paper/2204.10836/) (2022)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Recent Methodological Advances in Federated Learning for Healthcare](https://mlsanity.com/paper/2310.02874/) (2023)
- [FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection](https://mlsanity.com/paper/2408.09227/) (2024)
- [Cross-Domain Federated Learning in Medical Imaging](https://mlsanity.com/paper/2112.10001/) (2021)
- [Feasibility of Federated Learning from Client Databases with Different Brain Diseases and MRI Modalities](https://mlsanity.com/paper/2406.11636/) (2024)
- [Navigating Distribution Shifts in Medical Image Analysis: A Survey](https://mlsanity.com/paper/2411.05824/) (2024)
- [Federated Learning for the Classification of Tumor Infiltrating Lymphocytes](https://mlsanity.com/paper/2203.16622/) (2022)
- [Generalizable multi-task, multi-domain deep segmentation of sparse pediatric imaging datasets via multi-scale contrastive regularization and multi-joint anatomical priors](https://mlsanity.com/paper/2207.13502/) (2022)
- [Unifying and Personalizing Weakly-supervised Federated Medical Image Segmentation via Adaptive Representation and Aggregation](https://mlsanity.com/paper/2304.05635/) (2023)
- [MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning](https://mlsanity.com/paper/2205.01509/) (2022)
- [Personalizing Federated Instrument Segmentation with Visual Trait Priors in Robotic Surgery](https://mlsanity.com/paper/2408.03208/) (2024)
- [Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models](https://mlsanity.com/paper/2406.02653/) (2024)
- [Federated learning, ethics, and the double black box problem in medical AI](https://mlsanity.com/paper/2504.20656/) (2025)
- [SegViz: A federated-learning based framework for multi-organ segmentation on heterogeneous data sets with partial annotations](https://mlsanity.com/paper/2301.07074/) (2023)
- [Deep Anatomical Federated Network (Dafne): An open client-server framework for the continuous, collaborative improvement of deep learning-based medical image segmentation](https://mlsanity.com/paper/2302.06352/) (2023)
- [SoK: Assessing the State of Applied Federated Machine Learning](https://mlsanity.com/paper/2308.02454/) (2023)
- [Tackling Heterogeneity in Medical Federated learning via Vision Transformers](https://mlsanity.com/paper/2310.09444/) (2023)
- [MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation](https://mlsanity.com/paper/2410.22223/) (2024)

## 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.