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@Radiology_AI 1 year ago
The challenges of training a very large medical imaging #transformer model https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #BERT #ML #MachineLearning https://t.co/y3sZ38NIZ7
@Radiology_AI 1 year ago
The challenges of training a very large medical imaging #transformer model https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #DeepLearning #ML #MachineLearning https://t.co/zkCddWgOZR
@Radiology_AI 1 year ago
The challenges of training a very large medical imaging #transformer model https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #BERT #AI #MachineLearning https://t.co/SlT3r4lHqe
@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 #Transformers #ML #MachineLearning https://t.co/Yjh54EzRFg
@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 #BERT #ML #MachineLearning https://t.co/d4DFnYAKqL
@Radiology_AI 1 year ago
The challenges of training a very large medical imaging #transformer model https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #AI #ML #MachineLearning https://t.co/ij1qLOqOrw
@Radiology_AI 1 year ago
The challenges of training a very large medical imaging #transformer model https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #Transformers #AI #MachineLearning https://t.co/68vIIKCDag
@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 #ML #MachineLearning https://t.co/xAASWKqFag
@Radiology_AI 1 year ago
The challenges of training a very large medical imaging #transformer model https://doi.org/10.1148/ryai.210284 @StanfordRad @StanfordMed #BERT #DeepLearning #MachineLearning #AMIA2022 https://t.co/LvndChK6Cw
@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
@woojinrad 1 year ago
AI for medical imaging must be monitored for bias. Monitoring for bias will become more and more essential, just as monitoring for drift and #MachineLearning model decay. https://buff.ly/3VcUMBE @AuntMinnie #ImagingAI #AI #AIbias #ImagingInformatics https://t.co/d9SMbVuTWq
@yourICM 3 years ago
5 key principles that should be remembered for smart use of #ArtificialIntelligence as adoption of #MachineLearning systems becomes more widespread in medical imaging: ➡️knowledge base ➡️metrics ➡️bias ➡️model fragility ➡️error-prone behavior #DataScience https://rdcu.be/cdI0d https://t.co/OvNVtwfl94
@gp_pulipaka 4 years ago
#DeepLearning and Medical Imaging — Interpret What The Model Sees? #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #HealthTech http://bit.ly/2PEZCYe https://t.co/6vSuzvxjnW