Papers from #DeepMind panel at #NIPS2018
Work on radiotherapy planning: https://arxiv.org/abs/1809.04430
Triaging eye diseases: https://www.nature.com/articles/s41591-018-0107-6
Probabilistic U-net: https://arxiv.org/abs/1806.05034
#segmentation #CV #Unet
Work on radiotherapy planning: https://arxiv.org/abs/1809.04430
Triaging eye diseases: https://www.nature.com/articles/s41591-018-0107-6
Probabilistic U-net: https://arxiv.org/abs/1806.05034
#segmentation #CV #Unet
SAUNet: Shape Attentive U-Net for Interpretable Medical Image Segmentation
New approach for interpreting medical image segmentation models.
U-Net and other image segmentation models work quite well on medical data, but still aren't widely adopted. One of the reasons is the lack of reproducibility as well as robustness issues.
The key idea of the paper is using the additional stream in U-Net with shape features to increase robustness and use the output of this stream (attention map) that can be used or interpretability.
Modifications to the basic U-Net architecture:
- use dense blocks from DenseNet-121 as the encoder.
- use dual attention decoder block (with spatial and channel-wise attention paths)
- make the second stream using object shape (contour)
- dual-task loss function: cross-entropy + dice + edge loss (bce loss of the predicted shape boundaries)
Shape and spatial attention maps can be used for interpretation.
Paper: https://arxiv.org/abs/2001.07645
Code: https://github.com/sunjesse/shape-attentive-unet
#unet #imagesegmentation #interpretability #segmentation
New approach for interpreting medical image segmentation models.
U-Net and other image segmentation models work quite well on medical data, but still aren't widely adopted. One of the reasons is the lack of reproducibility as well as robustness issues.
The key idea of the paper is using the additional stream in U-Net with shape features to increase robustness and use the output of this stream (attention map) that can be used or interpretability.
Modifications to the basic U-Net architecture:
- use dense blocks from DenseNet-121 as the encoder.
- use dual attention decoder block (with spatial and channel-wise attention paths)
- make the second stream using object shape (contour)
- dual-task loss function: cross-entropy + dice + edge loss (bce loss of the predicted shape boundaries)
Shape and spatial attention maps can be used for interpretation.
Paper: https://arxiv.org/abs/2001.07645
Code: https://github.com/sunjesse/shape-attentive-unet
#unet #imagesegmentation #interpretability #segmentation
Live U-Net implementation online session today
Famous Abhishek Thakur (First 4x GM on Kaggle) is going to show you how to implement the original U-Net with #PyTorch.
Session starts in 4 hours from now (at 6PM CET / 9.30PM IST), make sure you turned the notifications on if you are interested.
YouTube Link: https://www.youtube.com/watch?v=u1loyDCoGbE
#Livecoding #Unet
Famous Abhishek Thakur (First 4x GM on Kaggle) is going to show you how to implement the original U-Net with #PyTorch.
Session starts in 4 hours from now (at 6PM CET / 9.30PM IST), make sure you turned the notifications on if you are interested.
YouTube Link: https://www.youtube.com/watch?v=u1loyDCoGbE
#Livecoding #Unet
YouTube
Implementing original U-Net from scratch using PyTorch
In this video, I show you how to implement original UNet paper using PyTorch. UNet paper can be found here: https://arxiv.org/abs/1505.04597
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
Implementing original #UNet paper using #PyTorch
Video tutorial on how to code your own neural network from scratch.
Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597
Video tutorial on how to code your own neural network from scratch.
Link: https://www.youtube.com/watch?v=u1loyDCoGbE&t=1s
Paper: https://arxiv.org/abs/1505.04597
YouTube
Implementing original U-Net from scratch using PyTorch
In this video, I show you how to implement original UNet paper using PyTorch. UNet paper can be found here: https://arxiv.org/abs/1505.04597
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
Please subscribe and like the video to help me keep motivated to make awesome videos like this one. :)
To buy my…
🦜 Hi!
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
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ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
We are the first Telegram Data Science channel.
Channel was started as a collection of notable papers, news and releases shared for the members of Open Data Science (ODS) community. Through the years of just keeping the thing going we grew to an independent online Media supporting principles of Free and Open access to the information related to Data Science.
Ultimate Posts
* Where to start learning more about Data Science. https://github.com/open-data-science/ultimate_posts/tree/master/where_to_start
* @opendatascience channel audience research. https://github.com/open-data-science/ods_channel_stats_eda
Open Data Science
ODS.ai is an international community of people anyhow related to Data Science.
Website: https://ods.ai
Hashtags
Through the years we accumulated a big collection of materials, most of them accompanied by hashtags.
#deeplearning #DL — post about deep neural networks (> 1 layer)
#cv — posts related to Computer Vision. Pictures and videos
#nlp #nlu — Natural Language Processing and Natural Language Understanding. Texts and sequences
#audiolearning #speechrecognition — related to audio information processing
#ar — augmeneted reality related content
#rl — Reinforcement Learning (agents, bots and neural networks capable of playing games)
#gan #generation #generatinveart #neuralart — about neural artt and image generation
#transformer #vqgan #vae #bert #clip #StyleGAN2 #Unet #resnet #keras #Pytorch #GPT3 #GPT2 — related to special architectures or frameworks
#coding #CS — content related to software engineering sphere
#OpenAI #microsoft #Github #DeepMind #Yandex #Google #Facebook #huggingface — hashtags related to certain companies
#productionml #sota #recommendation #embeddings #selfdriving #dataset #opensource #analytics #statistics #attention #machine #translation #visualization
Chats
- Data Science Chat https://t.me/datascience_chat
- ODS Slack through invite form at website
ODS resources
* Main website: https://ods.ai
* ODS Community Telegram Channel (in Russian): @ods_ru
* ML trainings Telegram Channel: @mltrainings
* ODS Community Twitter: https://twitter.com/ods_ai
Feedback and Contacts
You are welcome to reach administration through telegram bot: @opendatasciencebot
GitHub
ultimate_posts/where_to_start at master · open-data-science/ultimate_posts
Ultimate posts for opendatascience telegram channel - open-data-science/ultimate_posts