# IPMN Risk Assessment under Federated Learning Paradigm

arXiv: https://arxiv.org/abs/2411.05697 · HTML: https://mlsanity.com/paper/2411.05697/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 25 references that are in the map.
- First posted on arXiv 8 Nov 2024.

## Builds on (most cited first, 20 of 20)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Federated Optimization in Heterogeneous Networks](https://mlsanity.com/paper/1812.06127/) (2018)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization](https://mlsanity.com/paper/2007.07481/) (2020)
- [MONAI: An open-source framework for deep learning in healthcare](https://mlsanity.com/paper/2211.02701/) (2022)
- [FedAvg with Fine Tuning: Local Updates Lead to Representation Learning](https://mlsanity.com/paper/2205.13692/) (2022)
- [Fair Federated Medical Image Segmentation via Client Contribution Estimation](https://mlsanity.com/paper/2303.16520/) (2023)
- [On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond](https://mlsanity.com/paper/2206.05187/) (2022)
- [Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation](https://mlsanity.com/paper/2203.06338/) (2022)
- [DIGEST: Fast and Communication Efficient Decentralized Learning with Local Updates](https://mlsanity.com/paper/2307.07652/) (2023)
- [INN: Inflated Neural Networks for IPMN Diagnosis](https://mlsanity.com/paper/1907.00437/) (2019)
- [Federated Momentum Contrastive Clustering](https://mlsanity.com/paper/2206.05093/) (2022)
- [Frequency-Based Federated Domain Generalization for Polyp Segmentation](https://mlsanity.com/paper/2410.02044/) (2024)
- [Neural Transformers for Intraductal Papillary Mucosal Neoplasms (IPMN) Classification in MRI images](https://mlsanity.com/paper/2206.10531/) (2022)
- [Deep Multi-Modal Classification of Intraductal Papillary Mucinous Neoplasms (IPMN) with Canonical Correlation Analysis](https://mlsanity.com/paper/1710.09779/) (2017)

## Built on by (most cited first, 2 of 2)

- [Pancreas Part Segmentation under Federated Learning Paradigm](https://mlsanity.com/paper/2509.23562/) (2025)
- [Federated Breast Cancer Detection Enhanced by Synthetic Ultrasound Image Augmentation](https://mlsanity.com/paper/2506.23334/) (2025)

## Abstract (arXiv)

Accurate classification of Intraductal Papillary Mucinous Neoplasms (IPMN) is essential for identifying high-risk cases that require timely intervention. In this study, we develop a federated learning framework for multi-center IPMN classification utilizing a comprehensive pancreas MRI dataset. This dataset includes 652 T1-weighted and 655 T2-weighted MRI images, accompanied by corresponding IPMN risk scores from 7 leading medical institutions, making it the largest and most diverse dataset for IPMN classification to date. We assess the performance of DenseNet-121 in both centralized and federated settings for training on distributed data. Our results demonstrate that the federated learning approach achieves high classification accuracy comparable to centralized learning while ensuring data privacy across institutions. This work marks a significant advancement in collaborative IPMN classification, facilitating secure and high-accuracy model training across multiple centers.