It's official - #DHLLaunchpad Cohort 6 is here! 🚀
We are excited to introduce the 23 #digitalhealth companies selected for Cohort 6 of our Launchpad programme.
Find out how their digital #innovations are poised to transform #health and #care:
https://t.co/OghfywUlW4
If you're in Warsaw around October 26-29, don't miss one of Poland's biggest ML conferences, and a chance to chat with Amazon scientists.
@AmazonScience x @AmazonNewsPL x @MLinPL
🚨 New dataset! I'm releasing a synthetic chest X-ray dataset on @huggingface datasets.
This was created as part of a paper entitled Privacy Distillation (more details soon).
We ensured the images can NOT be used for re-identification!
https://t.co/5oNViSdPgE
Finally, MIMIC-IV-Note is what we've just released. Deidentified free-text clinical notes for everyone in MIMIC-IV; specifically discharge summaries (330k) and radiology reports (2.5m) - around 600 million tokens in the discharge summaries alone.
https://t.co/JL0QcP3SF0
Currently seeking a software engineer to join my team working on AI for healthcare. If you're looking to work in a dynamic multidisciplinary environment on building transformative tools and technologies, this could be for you!
Please retweet widely. Looking for a postdoc in representation learning. We want to learn good representations with (causal) generative models with rare/imbalanced data. You will work with bright minds, on a prestigious grant and interact with industry. DM me for more info.
I'm very proud to say our paper "Sketched RT3D: how to reconstruct billions of photons per second" received the best student paper at @ieeeICASSP this year. Thanks to my co-authors @TachellaJulian and Mike Davies for their great work. Check it out here - https://t.co/GwP5zuNrap
(1/3) I am happy to share that our paper "Diffusion Causal Models for Counterfactual Estimation" will be presented next week at @CLeaR_2022. Check poster session 1 (11/04 - 4pm) or our 90s video! Huge thanks to @STsaftaris and the rest of the team at https://t.co/ZkaE9rqdfs!
Hey #ISBI2022! Are you keen on learning how we can classify chest X-rays like radiologists?
Join the oral session tomorrow (30th March) in Multimodal Fusion at 5:30 PM (IST) / 12:00 PM (UTC) to learn more!
📄 https://t.co/6xiJZImdLF
🎮 https://t.co/HSe8PZkwoP
Please spread widely: PhD position in causality, representation learning and applications in healthcare. (funded by @CanonMedicalEDI@RAEngNews). Co-supervised by @alisonQoneil. Come and join @SnchzPedro_ @Xiaoliu33745946 @jacenkow and the VIOS team.
https://t.co/OM2Dcsrqbd
Please spread widely: Interesting in causality and in applications in healthcare. Looking for a PhD student (co-supervised with @ewenharrison) to join our group as part of @acrcedincare. https://t.co/T0Lgfb9PVW
Learn about disentangled representations and medical imaging at the 2nd DREAM tutorial at #miccai2021. This edition, after hugely successful 1st run, has more speakers, causality, new models, practical examples, and notes. https://t.co/FI0VG6GgPr @MICCAI_Society@MiccaiStudents
So imagine you can generate rare diseases or conditions at scale... mix different heart parts between patients to generate new example using disentangled anatomy arithmetic 🤯 #MICCAI2021
Our paper on performing arithmetic operations with disentangled *spatial* and *semantic* anatomical factors has been accepted in #MICCAI2021.
We investigate the use of this arithmetic in controllable cardiac image synthesis.
arxiv: https://t.co/Hg4fooTOaS
Hot off the press! My fabulous colleagues (@Xiaoliu33745946, @spirosthermos) proposed the first method to combine meta-learning and disentanglement. Why? Better performance on unseen domains (e.g. images from different scanners or hospitals). https://t.co/OaGoykjtiX #MICCAI2021