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@Radiology_AI 10 months ago
Differences in data distribution may affect federated #DeepLearning model performance in medical image segmentation https://doi.org/10.1148/ryai.220082 @ND_CSE @hku_science @ZJU_China #FederatedLearning #AI #MachineLearning #AIME23 #AIME2023 https://t.co/Sg0Y2jheGy
@Radiology_AI 1 year ago
A large-scale 3D #transformer model could reduce the amount of data needed to train and fine-tune medical imaging #AI models https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #BERT #DeepLearning #MachineLearning https://t.co/924nXgpWwb
@Radiology_AI 1 year ago
A large-scale 3D #transformer model could reduce the amount of data needed to train and fine-tune medical imaging #AI models https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #DeepLearning #ML #MachineLearning https://t.co/cfC4OrFxC0
@Radiology_AI 1 year ago
A large-scale 3D #transformer model could reduce the amount of data needed to train and fine-tune medical imaging #AI models https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #BERT #DeepLearning #MachineLearning https://t.co/DVl9XtgTBI
@Radiology_AI 1 year ago
A large-scale 3D #transformer model could reduce the amount of data needed to train and fine-tune medical imaging #AI models https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #DeepLearning #ML #MachineLearning https://t.co/t0Vza8lPaU
@Michael_D_Moor 2 years ago
Preprint alert! Interested in #MachineLearning in #medicine and #sepsis? We developed and validated a #DeepLearning model for predicting sepsis across 5 ICU databases from 3 countries featuring 156k ICU stays and 783 patient years worth of monitoring data. A 🧵. 1/n https://t.co/4RL3UaXYIY