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20162025
most citedImage biomarker standardisation initiative

4.2k citations · 5.1k across the 27 of their papers we have counts for

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8 papers · 1 filter

eess.IV2025★ 1 cited

BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis

Florian Kofler, Marcel Rosier, Mehdi Astaraki +34

The Brain Tumor Segmentation (BraTS) cluster of challenges has significantly advanced brain tumor image analysis by providing large, curated datasets and addressing clinically rele…

eess.IV2024★ 14 cited

BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023

Anahita Fathi Kazerooni, Nastaran Khalili, Xinyang Liu +77

Pediatric central nervous system tumors are the leading cause of cancer-related deaths in children. The five-year survival rate for high-grade glioma in children is less than 20%.…

eess.IV2024★ 9 cited

Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

Dominic LaBella, Ujjwal Baid, Omaditya Khanna +119

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in…

eess.IV2023★ 4 cited

The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting

Florian Kofler, Felix Meissen, Felix Steinbauer +103

A myriad of algorithms for the automatic analysis of brain MR images is available to support clinicians in their decision-making. For brain tumor patients, the image acquisition ti…

eess.IV2022

MammoFL: Mammographic Breast Density Estimation using Federated Learning

Ramya Muthukrishnan, Angelina Heyler, Keshava Katti +11

In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-ins…

eess.IV2022★ 11 cited

Federated Learning for the Classification of Tumor Infiltrating Lymphocytes

Ujjwal Baid, Sarthak Pati, Tahsin M. Kurc +6

We evaluate the performance of federated learning (FL) in developing deep learning models for analysis of digitized tissue sections. A classification application was considered as…

eess.IV2021★ 16 cited

The Federated Tumor Segmentation (FeTS) Challenge

Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…

eess.IV2019★ 6 cited

Accurate and Robust Alignment of Variable-stained Histologic Images Using a General-purpose Greedy Diffeomorphic Registration Tool

Ludovic Venet, Sarthak Pati, Paul Yushkevich +1

Variously stained histology slices are routinely used by pathologists to assess extracted tissue samples from various anatomical sites and determine the presence or extent of a dis…