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Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

Federated learning (FL) algorithms have emerged as a promising solution to train models for healthcare imaging across institutions while preserving privacy. Here, the authors describe the Federated Tumor Segmentation (FeTS) challenge for the decentralised benchmarking of FL algorithms and evaluation of Healthcare AI algorithm generalizability in real-world cancer imaging datasets.

Maximilian Zenk
Ujjwal Baid
Spyridon Bakas
ResearchOpen Access08 Jul 2025
Nature Communications

Volume: 16, P: 1-20
An objective comparison of cell-tracking algorithms

This analysis describes the results of three Cell Tracking Challenge editions for examining the performance of cell segmentation and tracking algorithms and provides practical feedback for users and developers.

Vladimír Ulman
Martin Maška
Carlos Ortiz-de-Solorzano
Research30 Oct 2017
Nature Methods

Volume: 14, P: 1141-1152
Resprouting trees drive understory vegetation dynamics following logging in a temperate forest

Radim Matula
Radomír Řepka
Petr Maděra
ResearchOpen Access08 Jun 2020
Scientific Reports

Volume: 10, P: 1-10
Federated learning enables big data for rare cancer boundary detection

Federated ML (FL) provides an alternative to train accurate and generalizable ML models, by only sharing numerical model updates. Here, the authors present the largest FL study to-date to generate an automatic tumor boundary detector for glioblastoma.

Sarthak Pati
Ujjwal Baid
Spyridon Bakas
ResearchOpen Access05 Dec 2022
Nature Communications

Volume: 13, P: 1-17
The Cell Tracking Challenge: 10 years of objective benchmarking

This updated analysis of the Cell Tracking Challenge explores how algorithms for cell segmentation and tracking in both 2D and 3D have advanced in recent years, pointing users to high-performing tools and developers to open challenges.

Martin Maška
Vladimír Ulman
Carlos Ortiz-de-Solórzano
ResearchOpen Access18 May 2023
Nature Methods

Volume: 20, P: 1010-1020
Topology-preserving contourwise shape fusion

Aleksandra Melnikova
Martin Maška
Petr Matula
ResearchOpen Access28 Mar 2025
Scientific Reports

Volume: 15, P: 1-12
Author Correction: Federated learning enables big data for rare cancer boundary detection

Sarthak Pati
Ujjwal Baid
Spyridon Bakas
Amendments and CorrectionsOpen Access26 Jan 2023
Nature Communications

Volume: 14, P: 1-2

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Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge

An objective comparison of cell-tracking algorithms

Resprouting trees drive understory vegetation dynamics following logging in a temperate forest

Federated learning enables big data for rare cancer boundary detection

The Cell Tracking Challenge: 10 years of objective benchmarking

Topology-preserving contourwise shape fusion

Author Correction: Federated learning enables big data for rare cancer boundary detection

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