25 papers
Re-thinking Mammography Transfer Learning: The Dataset-Informed Transfer Learning (DITL) Framework for Breast Cancer Screening and Lesion Diagnosis
Adarsh Bhandary Panambur, Siming Bayer, Andreas Maier
Enhancing classification performance in mammography remains a persistent challenge across both small curated datasets and large-scale clinical cohorts. Conventional transfer learni…
Agentic Autoresearch for CT Reconstruction
Andreas Maier, Lucas Kachelriess, Siming Bayer +4
Comparing CT reconstruction methods fairly is labor-intensive and largely manual, and many benchmarks use idealized data. We ask whether a large language model (LLM) agent can do t…
Robustness and Stability Analysis of Differentiable Shift-Variant FBP for Cone-Beam CT under Challenging Acquisition Settings
Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +5
The differentiable shift-variant filtered backprojection (SV-FBP) framework enables data-driven estimation of redundancy weights for cone-beam CT reconstruction under general sourc…
WING: A Window-Prior-Based Generative Network with Gated Inception for Cross-Modality CT Synthesis
Siyuan Mei, Yan Xia, Yipeng Sun +7
Generating CT volumes from MRI and CBCT can improve treatment planning in adaptive radiotherapy while avoiding additional radiation exposure. However, direct regression of CT inten…
PROTECT-90: A Fault Dataset for Power System Protection
Julian Oelhaf, Georg Kordowich, Christian Bergler +3
The increasing interest in data-driven methods for power system protection is accompanied by a lack of standardized, publicly available high-voltage waveform datasets that enable t…
Fault Inception Detection in Real-World Disturbance Data for Power System Protection
Julian Oelhaf, Mehran Pashaei, Paula Andrea Perez-Toro +5
Large collections of real-world disturbance recordings are increasingly available in transmission networks, but their value for power system protection and automated disturbance an…