# Automated Pancreas Segmentation Using Multi-institutional Collaborative Deep Learning

arXiv: https://arxiv.org/abs/2009.13148 · HTML: https://mlsanity.com/paper/2009.13148/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [The Federated Tumor Segmentation (FeTS) Challenge](https://mlsanity.com/paper/2105.05874/) (2021)
- [Federated Learning for Medical Applications: A Taxonomy, Current Trends, Challenges, and Future Research Directions](https://mlsanity.com/paper/2208.03392/) (2022)
- [Multi-task Federated Learning for Heterogeneous Pancreas Segmentation](https://mlsanity.com/paper/2108.08537/) (2021)
- [FedSLD: Federated Learning with Shared Label Distribution for Medical Image Classification](https://mlsanity.com/paper/2110.08378/) (2021)
- [FedDropoutAvg: Generalizable federated learning for histopathology image classification](https://mlsanity.com/paper/2111.13230/) (2021)
- [Federated Learning for Cyber Physical Systems: A Comprehensive Survey](https://mlsanity.com/paper/2505.04873/) (2025)
- [Vulnerability Due to Training Order in Split Learning](https://mlsanity.com/paper/2103.14291/) (2021)
- [Federated Learning for Large Models in Medical Imaging: A Comprehensive Review](https://mlsanity.com/paper/2508.20414/) (2025)
- [Federated learning, ethics, and the double black box problem in medical AI](https://mlsanity.com/paper/2504.20656/) (2025)
- [SoK: Assessing the State of Applied Federated Machine Learning](https://mlsanity.com/paper/2308.02454/) (2023)
- [Automatic segmentation of colorectal liver metastases for ultrasound-based navigated resection](https://mlsanity.com/paper/2511.05253/) (2025)

## Abstract (arXiv)

The performance of deep learning-based methods strongly relies on the number of datasets used for training. Many efforts have been made to increase the data in the medical image analysis field. However, unlike photography images, it is hard to generate centralized databases to collect medical images because of numerous technical, legal, and privacy issues. In this work, we study the use of federated learning between two institutions in a real-world setting to collaboratively train a model without sharing the raw data across national boundaries. We quantitatively compare the segmentation models obtained with federated learning and local training alone. Our experimental results show that federated learning models have higher generalizability than standalone training.