4 citations · 4 across the 7 of their papers we have counts for
8 papers
Anguinus Sculpturae: Compositional Synthesis of Peak-Enhancement Breast DCE-MRI Scans
Benjamin Hamm, Nico Albert Disch, Maximilian Rokuss +3
Dynamic contrast-enhanced breast MRI (DCE-MRI) is rich in anatomical and perfusion information, but its reliance on gadolinium-based contrast agents raises safety concerns and adds…
Exploiting Longitudinal Context in Clinician-Verified Interactive Lesion Tracking
Yannick Kirchhoff, Maximilian Rokuss, Daniel Philipp Mertens +5
Tracking tumor lesions across serial CT scans is essential for oncological response assessment. Existing automated methods face a fundamental trade-off: end-to-end trackers achieve…
Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
TriALS: Triphasic-Aided Liver Lesion Segmentation Benchmark in Non-Contrast CT
Marawan Elbatel, Mohamed Ghonim, Jiaji Mao +62
Automated segmentation of liver lesions on non-contrast computed tomography (NCCT) is clinically important but fundamentally challenging, particularly in low-resource settings acro…
CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series
Nico Albert Disch, Saikat Roy, Constantin Ulrich +5
Forecasting how 3D medical scans evolve over time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a singl…
MeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI
Benjamin Hamm, Yannick Kirchhoff, Maximilian Rokuss +1
The ODELIA Breast MRI Challenge 2025 addresses a critical issue in breast cancer screening: improving early detection through more efficient and accurate interpretation of breast M…
Advances in Automated Fetal Brain MRI Segmentation and Biometry: Insights from the FeTA 2024 Challenge
Vladyslav Zalevskyi, Thomas Sanchez, Misha Kaandorp +67
Accurate fetal brain tissue segmentation and biometric analysis are essential for studying brain development in utero. The FeTA Challenge 2024 advanced automated fetal brain MRI an…
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
Santhosh Parampottupadam, Melih Coşğun, Sarthak Pati +7
Background: Federated learning (FL) enables collaborative training of clinical AI models without centralizing patient data, but adoption is limited by privacy concerns, heterogeneo…