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Found 6 papers, 2 papers with code
Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma
Consistent performance was observed across the three independent multicenter test sets with Cdt values of 0. 707 (UPenn-GBM, internal test set), 0. 672 (UCSF-PDGM, first external test set) and 0. 618 (RHUH-GBM, second external test set).
Multicenter Privacy-Preserving Model Training for Deep Learning Brain Metastases Autosegmentation
When the UKER pretrained model is applied to USZ, LWF achieves a higher average F1 score (0. 839) than naive TL (0. 570) and single-center training (0. 688) on combined UKER and USZ test data.
A Survey of Incremental Transfer Learning: Combining Peer-to-Peer Federated Learning and Domain Incremental Learning for Multicenter Collaboration
Incremental transfer learning, which combines peer-to-peer federated learning and domain incremental learning, can overcome the data privacy issue and meanwhile preserve model performance by using continual learning techniques.
Deep Learning for Cancer Prognosis Prediction Using Portrait Photos by StyleGAN Embedding
The StyleGAN2 is then used to embed the photographs to its highly expressive latent space.
Benchmarking ChatGPT-4 on ACR Radiation Oncology In-Training (TXIT) Exam and Red Journal Gray Zone Cases: Potentials and Challenges for AI-Assisted Medical Education and Decision Making in Radiation Oncology
The potential of large language models in medicine for education and decision making purposes has been demonstrated as they achieve decent scores on medical exams such as the United States Medical Licensing Exam (USMLE) and the MedQA exam.
The Segment Anything foundation model achieves favorable brain tumor autosegmentation accuracy on MRI to support radiotherapy treatment planning
Conclusions: The Segment Anything foundation model, while trained on photos, can achieve high zero-shot accuracy for glioma brain tumor segmentation on MRI slices.
