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Advancing Real-World Federated Learning in Radiology
Federated Learning (FL) enables collaborative model training without data centralization – a crucial aspect for radiological image analysis where…
RACOON: A Guide to Bridging the Gap Between Simulated and Real-World Federated Learning Research
Deep learning (DL) has become an important part of radiological image analysis. To train these deep-learning models, access to large and diverse…
Research with mint Data Collection AI
ESGAR 2024
35th Annual Meeting and Postgraduate Course of The European Society of Gastrointestinal and Abdominal Radiology
On-Site BZKF BORN Roll-Out Trainings Keep on Going
Our expert Steffen Rupp recently visited the Technical University of Munich to continue the on-site BZKF BORN Roll-Out Trainings. As mentioned…
Unveiling the Predictive Power of Pericardial Effusion in COVID-19 Outcomes
The COVID-19 pandemic wreaked havoc on healthcare systems worldwide, requiring a comprehensive understanding of disease progression for optimal…
Advancing Childhood Cancer Research: The Role of Technology in Brain Tumor Diagnosis and Treatment
Brain cancers account for more than 15% of childhood cancers and are the second most common childhood cancer [1]. Mint Medical is Going Gray in May…
RÖKO 2024
105th German Radiology Congress and 10th Joint Congress of the DRG and the ÖRG
Unlocking the Power of AI: The Vital Role of Structured Reporting
We all know the phrase “a picture is worth a thousand words”; yet, in specific fields, one thousand words are simply too many and can potentially…
JPR 2024
The 54th Jornada Paulista de Radiologia