From the 2 of 7 linked papers with an AI index.
7 papers
Privacy Leakage in Federated Learning in Radiology Reports: A Comparative Evaluation of Tokenizer-Driven Privacy Risks
Santhosh Parampottupadam, Andres Martinez, Dimitrios Bounias +3
The paper measures how much patient information can be reconstructed from model gradients in federated learning of radiology reports, comparing three different tokenizers and showi…
Inclusive Federated Learning Through Compliance-Weighted Noise Allocation in Healthcare AI
Santhosh Parampottupadam, Melih CoÅÄun, Sarthak Pati +7
The paper proposes a federated learning framework that adjusts differential privacy noise based on each healthcare institution's compliance level, allowing lower‑compliance sites t…
Federated Medical Image Segmentation under Real-World Label Noise: A Benchmark Suite for Noisy Label Learning Method Selection
Markus Bujotzek, Dimitrios Bounias, Stefan Denner +4
While federated learning (FL) enables collaborative medical image segmentation without centralizing sensitive data, real-world deployment is frequently complicated by cross-site la…
Kaapana: A Comprehensive Open-Source Platform for Integrating AI in Medical Imaging Research Environments
Ãnal Akünal, Markus Bujotzek, Stefan Denner +8
Developing generalizable AI for medical imaging requires both access to large, multi-center datasets and standardized, reproducible tooling within research environments. However, l…
Gut decisions based on the liver: A radiomics approach to boost colorectal cancer screening
Anna Hinterberger, Jonas Bohn, Dasha Trofimova +13
Non-invasive colorectal cancer (CRC) screening represents a key opportunity to improve colonoscopy participation rates and reduce CRC mortality. This study explores the potential o…
The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection
Katharina Eckstein, Constantin Ulrich, Michael Baumgartner +5
Large-scale pre-training holds the promise to advance 3D medical object detection, a crucial component of accurate computer-aided diagnosis. Yet, it remains underexplored compared…