add pub list (#1090)

Author: holgerroth

README.md

@@ -76,6 +76,10 @@ pip install nvflare
    To migrate from releases prior to 2.2.1, here are few notes that might help
    [migrate to 2.2.1](docs/release_notes/2.2.1/migration_notes.md).
 
+### Related talks and publications
+
+For a list of talks, blogs, and publications related to NVIDIA FLARE, see [here](docs/publications_and_talks.md).
+
 ## Third party license
 
 See 3rdParty folder for their license files.

docs/publications_and_talks.md

@@ -0,0 +1,60 @@
+## Publications
+Non-exhaustive list of papers and publications related to NVIDIA FLARE, 
+including papers using NVIDIA FLARE's predecessor libraries included in the [Clara Train SDK](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/clara-train-sdk).
+
+#### 2022
+* **2022-12** [FLARE: Federated Learning from Simulation to Real-World](https://arxiv.org/abs/2210.13291) ([International Workshop on Federated Learning, NeurIPS 2022, New Orleans, USA](https://federated-learning.org/fl-neurips-2022))
+* **2022-10** [Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation](https://arxiv.org/abs/2203.06338) ([ECCV 2022](https://eccv2022.ecva.net/))
+* **2022-06** [Closing the Generalization Gap of Cross-silo Federated Medical Image
+Segmentation](https://openaccess.thecvf.com/content/CVPR2022/papers/Xu_Closing_the_Generalization_Gap_of_Cross-Silo_Federated_Medical_Image_Segmentation_CVPR_2022_paper.pdf) ([CVPR 2022](https://cvpr2022.thecvf.com/))
+
+#### 2021
+* **2021-04** [Federated semi-supervised learning for COVID region segmentation in chest CT using multi-national data from China, Italy, Japan](https://www.sciencedirect.com/science/article/pii/S1361841521000384) ([Medical Image Analysis](https://www.sciencedirect.com/journal/medical-image-analysis))
+* **2022-10** [Joint Multi Organ and Tumor Segmentation from Partial Labels Using Federated Learning](https://link.springer.com/chapter/10.1007/978-3-031-18523-6_6) ([DeCaF @ MICCAI 2022](https://decaf-workshop.github.io/decaf-2022/))
+* **2022-10** [Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-modal Brain Tumor Segmentation](https://arxiv.org/abs/2208.10553) ([DeCaF @ MICCAI 2022](https://decaf-workshop.github.io/decaf-2022/))
+* **2022-02** [Do Gradient Inversion Attacks Make Federated Learning Unsafe?](https://arxiv.org/abs/2202.06924) (Preprint)
+* **2021-10** [Federated whole prostate segmentation in mri with personalized neural architectures](https://arxiv.org/abs/2107.08111) ([MICCAI 2021](https://www.miccai2021.org/en/))
+* **2021-10** [Multi-task Federated Learning for Heterogeneous Pancreas Segmentation](https://arxiv.org/abs/2108.08537) ([DCL @ MICCAI 2021](https://dcl-workshop.github.io/))
+* **2021-09** [Federated learning for predicting clinical outcomes in patients with COVID-19](https://www.nature.com/articles/s41591-021-01506-3) ([Nature Medicine](https://www.nature.com/nm/))
+* **2021-06** [Federated learning improves site performance in multicenter deep learning without data sharing](https://academic.oup.com/jamia/article-abstract/28/6/1259/6127556) ([JAMIA](https://academic.oup.com/jamia))
+* **2021-04** [Auto-FedAvg: Learnable Federated Averaging for Multi-Institutional Medical Image Segmentation](https://arxiv.org/abs/2104.10195) (Preprint)
+
+#### 2020
+* **2020-10** [Federated Learning for Breast Density Classification: A Real-World Implementation](https://arxiv.org/abs/2009.01871) ([DCL @ MICCAI 2020](https://dcl-workshop.github.io/dcl2020/index.html))
+* **2020-10** [Automated pancreas segmentation using multi-institutional collaborative deep learning](https://arxiv.org/abs/2009.13148) ([DCL @ MICCAI 2020](https://dcl-workshop.github.io/dcl2020/index.html))
+
+#### 2019
+* **2019-10** [Privacy-preserving Federated Brain Tumour Segmentation](https://arxiv.org/abs/1910.00962) ([MLMI @ MICCAI 2019](https://mlmi2019.web.unc.edu/))
+
+## Blogs & Videos
+NVIDIA FLARE related blogs and other media.
+
+#### 2022
+* **2022-10** [Federated Learning from Simulation to Production with NVIDIA FLARE](https://developer.nvidia.com/blog/federated-learning-from-simulation-to-production-with-nvidia-flare/?ncid=so-nvsh-705336#cid=ix11_so-nvsh_en-us) (NVIDIA Technical Blog)
+* **2022-06** [Experimenting with Novel Distributed Applications Using NVIDIA Flare 2.1](https://developer.nvidia.com/blog/experimenting-with-novel-distributed-applications-using-nvidia-flare-2-1/) (NVIDIA Technical Blog)
+
+#### 2021
+* **2021-11** [Creating Robust and Generalizable AI Models with NVIDIA FLARE](https://developer.nvidia.com/blog/creating-robust-and-generalizable-ai-models-with-nvidia-flare/) (NVIDIA Technical Blog)
+* **2021-09** [Federated Learning for Medical AI and Triaging COVID-19 Patients](https://www.youtube.com/watch?v=cOXVrtkv6FE) (NVIDIA video)
+* **2021-06** [Federated Learning with Homomorphic Encryption](https://developer.nvidia.com/blog/federated-learning-with-homomorphic-encryption/) (NVIDIA Technical Blog)
+
+#### 2019
+* **2019-12** [Federated Learning powered by NVIDIA Clara](https://developer.nvidia.com/blog/federated-learning-clara/)
+* **2019-10** [NVIDIA Research: First Privacy-Preserving Federated Learning System for Medical Imaging](https://www.youtube.com/watch?v=Jy7ozgwovgg) (NVIDIA video)
+
+## Talks
+Recent talks and Webinars covering federated learning research and NVIDIA FLARE.
+
+#### 2022
+* **2022-10** [Modern Tools for Collaborative Medical Image Analysis](https://drive.google.com/file/d/1hmlyG7g1SU8vhQ5wdTFhkqFO9Ty8BiYG/view?usp=sharing) ([Keynote - DART @ MICCAI 2022](https://sites.google.com/view/dart2022/home?authuser=0)) 
+* **2022-07** [NVIDIA FLARE Tutorial for Beginners](https://www.youtube.com/watch?v=8x7oY3xAgek&t=11s&ab_channel=NVIDIADeveloper) (United Imaging Meetup)
+* **2022-07** [Techniques and Tools for Collaborative Development of AI Models across Institutes](https://www.bilibili.com/video/BV1y14y147nc/?spm_id_from=333.337.search-card.all.click) ([VALSE Webinar](http://valser.org/article-572-1.html))
+* **2022-04** [Advanced Techniques for Collaborative Development of AI Models for Medical Imaging](https://rensselaer.webex.com/recordingservice/sites/rensselaer/recording/dd67440ba9f2103abaf900505681a58c/playback) ([IEEE EMBS Webinar Series](https://sites.google.com/view/ieee-biip-webinars/webinar-speakers))
+* **2022-03** [NVIDIA FLARE: Federated Learning Application Runtime Environment for Developing Robust AI Models](https://youtu.be/lLeULNI1nT8) ([SFBigAnalytics Meetup](https://www.meetup.com/sf-big-analytics/?_cookie-check=Efm7MGh7mO4YiV8A))
+* **2022-01** [Techniques for Collaborative Development of AI Models in the Age of COVID-19](https://www.youtube.com/watch?v=ymfXmyuTvlA) ([MICCAI Industrial Talk Series](https://www.youtube.com/channel/UCLSO1_i9UtDGfsaKQyqhJTQ))
+])
+
+#### 2021
+* **2021-09** [Federated Learning](https://www.youtube.com/watch?v=YeYO4JGTBb0&amp) ([MONAI MICCAI Bootcamp 2021](https://www.gpuhackathons.org/event/monai-miccai-bootcamp-2021))
+* **2021-03** [NVIDIA FLARE: An Open Federated Learning Platform](https://www.nvidia.com/en-us/on-demand/session/gtcspring22-se1991/) ([GTC Spring 2022](https://www.nvidia.com/gtc/))
+* **2021-03** [Federated Learning for Healthcare – Collaborative AI without Sharing Patient Data ](https://www.youtube.com/watch?v=xr_eJp3ctzw) ([Data Science Seminar](https://www.dkfz.de/en/datascience/seminar/Rieke.html))