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The first batch of videos from our
#MICCAI2018 Tutorial on#DeepLearning for#MedicalImaging#DeepA2Z now available here: https://www.youtube.com/playlist?list=PLUHJoZihYwkmhAXME7LgCjhMnJWTufQHH …https://twitter.com/GlockerBen/status/1040941560186249218 …
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Implementation of graph saliency maps using spectral convolutional networks (https://arxiv.org/abs/1806.01764 ) is now available at https://github.com/sarslancs/graph_saliency_maps … + slides of my talk at
#GRAIL#MICCAI2018 are online too https://www.researchgate.net/profile/Salim_Arslan/publication/327867764_Slides/data/5baa4630299bf13e604b223a/slides.pdf …#opensource -
Wow wow wow. This went way beyond my expectations:
#MICCAI2018 Decathlon in Medical Image Segmentation http://medicaldecathlon.com rocked by the nnU-Net! Congrats especially to our Fabian "FabLabs" Isensee. So proud of this amazing team@mic_dkfzpic.twitter.com/c5D97QlXyN
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Thanks to everyone who made
#MICCAI2018 possible and for all people who attended the event. Hope everyone got back home safely!#Leeds#MICCAIpic.twitter.com/uGUIFLPyvr
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There are many reasons to be proud of our young vibrant team of
#AI and data scientists. Today: Yue Zhue received the Young Scientist Award at#MICCAI2018. Congrats! pic.twitter.com/50eixWN0Wp
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Our latest work on modeling infant body shape and motion from RGB-D just appeared at
#MICCAI2018. Video here: https://youtu.be/IhDnuQZ-c_s Model, pdf, etc:https://ps.is.tuebingen.mpg.de/publications/hesse-micai-2018 … -
Excellent work by Nicolas Toussaint from
@KingsImaging presented now at#DLMIA#MICCAI2018 on real time fetal organ detection in 2D ultrasound! pic.twitter.com/vBZnXPKPef
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8 orals, 17 posters, 5 keynotes, 12 algorithms to harmonise data from 15 subjects scanned with 4 MRI protocols... looking forward to
#CDMRI2018 and#MUSHAC today!@CD_MRI#MICCAI2018 pic.twitter.com/1HsUu80LUo
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#IntelAI Research introduces the first use of federated learning for multi-institutional collaboration, which enables#deeplearning modeling without sharing patient data and has achieved 99% of the model performance of a data-sharing model: https://intel.ly/2xk7be5#MICCAI2018 pic.twitter.com/EbsDRCprHo
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So excited to see the leaders of
@RadiologyACR Bob Allen and Mike Tilkin,@RSNA@cekahn Chuck Kahn at@MICCAI2018. They are here to talk to the great researchers working on#AI and#DeepLearning in#MedicalImaging at#MICCAI2018. Please reach out.@NvidiaAIpic.twitter.com/FRWPg46Wu6
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Presenting our paper about bias when using
#CycleGAN in#medical image translation at#MICCAI2018#MICCAI@MILAMontreal https://arxiv.org/abs/1805.08841 pic.twitter.com/jwhcYbZ1Q0
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The first Keynote at
#MICCAI2018 by@DrGMcGinty from@RadiologyACR: about#ArtificialIntelligence in#Radiologypic.twitter.com/j7R9hY3YCa
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Hey
#MICCAI2018 people. Want to reduce false-positives in fiber tractography? Learn how, at my poster T-74. You can't miss it, it's in the remotest corner possible near the terrace. pic.twitter.com/eOApeu675s
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Congratulations to Gavin and team from
@KingsImaging on obtaining the outstanding paper award at#MICCAI2018 AE-CAI workshop. Well done! pic.twitter.com/sTcZgoRIUw
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Artificial intelligence algorithm produces synthetic brain MR images, according to presentation at
#MICCAI2018 http://bit.ly/2xc6naU#MRI#radiologypic.twitter.com/KidPRFhOFe
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Great line-up of speakers at tomorrow's
#MICCAI2018#DeepLearning for#MedicalImaging Tutorial#DeepA2Z Come early, it will get busy! https://sites.google.com/view/miccai-dl-tutorial …pic.twitter.com/IjCoZsGVhT
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Getting ready for MICCAI 2018 in Granada
#MICCAI2018#Leedspic.twitter.com/DbN9Qd9S0Q
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Team MIC-DKFZ led by Fabian Isensee ranks first in 12/13 classes (according to dice score) at the Medical Segmentation Decathlon (> 100 participating teams, > 30 submitting teams). Excited to test generalization further on 3 mystery tasks!
#MICCAI2018 https://decathlon.grand-challenge.org/evaluation/results/ … -
Check out our new work on training segmentation networks with multiple datasets, each annotated with different label subsets! This will be presented at
#MLMI at#MICCAI2018. Congrats@JanaKemnitz!https://twitter.com/JanaKemnitz/status/1022014086207430657 …
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Check out our new work on scribble-supervised medical image segmentation
@dlmia_miccai https://arxiv.org/abs/1807.04668 . Learning from scribbles alone we get less than a 5% performance drop compared to full supervision. Also my first paper as last author!#MICCAI2018#MachineLearningpic.twitter.com/kKC7yCooij
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