Timo Kepp

Researcher at the Department for Artificial Intelligence in Medical Imaging @DFKI

[Universität Lübeck @UniLuebeck]

Zeit, neue Wege zu gehen: Wir beenden unsere Aktivitäten auf X. Bleiben Sie mit uns im Dialog – auf unseren anderen Kanälen. https://t.co/evTYoJxeLD
#eXit #WissXit #ByeByeX #QuitX

[SASHIMI Workshop @ MICCAI 2026 @SashimiWorkshop]

Huge congratulations to Joshua Niemeijer for winning best paper award! Thanks to everyone for coming and supporting each other today! Let’s see each other again in Daejeon! #MICCAI2024

[Noam Brown @polynoamial]

Today, I’m excited to share with you all the fruit of our effort at @OpenAI to create AI models capable of truly general reasoning: OpenAI's new o1 model series! (aka 🍓) Let me explain 🧵 1/

[Retinai @Retin_AI]

We will be presenting 2 studies at this year's @EURETINA conference in Barcelona! #EURETINA2024 #OphthalmologyResearch

1/7👇

[Jakob Wasserthal @JakobWasserthal]

Exciting news! We are releasing another major update to TotalSegmentator: It can now segment 16 different brain structures in CT images. Try out the new "brain_structures" task.

[Ryoma Sato @joisino_en]

My paper "Training-free Graph Neural Networks and the Power of Labels as Features" has been accepted to #TMLR 🎉

I proposed training-free (and optionally trained) GNNs.

Paper📜：https://t.co/J6rOQrGejo
Code📁：https://t.co/gEzmwu5N48

[AI at Meta @AIatMeta]

🆕 Research paper from GenAI at Meta: Imagine yourself: Tuning-Free Personalized Image Generation.

Research paper ➡️ https://t.co/8RlWdU5MKu

Want to try it? The feature is available now as a beta in Meta AI for users in the US.

[IEEE-TMI @IEEE_TMI]

Early Access Paper Just Posted: A Novel Poroelastography Method for High-quality Estimation of Lateral Strain, Solid Stress and Fluid Pressure In Vivo. Read the paper: https://t.co/yu87VSEJdi

[Gradio @Gradio]

Flux.1-DEV with ControlNet Canny!🔥🔥

[Valeriy M., PhD, MBA, CQF @predict_addict]

Hidden deep inside the depth of internet, a treasure.

#probability

[IEEE-TMI @IEEE_TMI]

Weakly supervised ROI proposal networks (WSRPN) provide a fully differentiable way to train object detection models with image-level labels only. They significantly improve over the previous state-of-the-art in pathology detection on chest radiographs. https://t.co/hxA3uMOJBP

[Stanford AI Lab @StanfordAILab]

arXiv -> alphaXiv

Students at Stanford have built alphaXiv, an open discussion forum for arXiv papers. @askalphaxiv

You can post questions and comments directly on top of any arXiv paper by changing arXiv to alphaXiv in any URL!

[Sayak Paul @RisingSayak]

With larger and larger diffusion transformers coming up, it's becoming increasingly important to have some good quantization tools for them.

We present our findings from a series of experiments on quantizing different diffusion pipelines based on diffusion transformers.

We demonstrate excellent memory savings with a bit of sacrifice on inference latency which is expected to improve in the coming days.

Diffusers 🤝 Quanto ❣️

🧵 1/7

[AI at Meta @AIatMeta]

Introducing Meta Segment Anything Model 2 (SAM 2) — the first unified model for real-time, promptable object segmentation in images & videos.

SAM 2 is available today under Apache 2.0 so that anyone can use it to build their own experiences

Details ➡️ https://t.co/eTTDpxI60h

[Yann LeCun @ylecun]

Meta Segment Anything Model v2 (SAM 2) is out.
Can segment images and videos.
Open source under Apache-2 license.
Web demo, paper, and datasets available.
Amazing performance.

[Vincent D. Warmerdam @fishnets88]

@jeremyphoward reached out to me yesterday asking me if I wanted to get a sneak peek at a new library he was working on.

I liked the experience so much that I ended up making an intro tutorial for it ... as well as a plugin 😅

https://t.co/G4jmFF5iOA

[Peyman Milanfar @docmilanfar]

“We fine tune the last layer”

[Johanna P Mueller @Hanna_Paulaluap]

🚀🩻 Great News! Our paper on Resource-efficient Forward-Forward networks without Back-propagation for Medical Imaging Analysis has been accepted to the #MLMI workshop at MICCAI 2024!🌍💫 @MICCAI_Society
@BorderlessSci @aibe_fau @UniFAU @BernhardKainz1
https://t.co/w7psMEwsnH

[Julia Schnabel @ja_schnabel]

Our own @hannah_eichhorn on PHIMO: Physics-Informed Motion Correction of GRE MRI for T2* Quantification 👏👏👏

@HelmholtzIml @NMRMgroup (#ChristinePreibisch)

[Pascal Fua @FuaPv]

In an upcoming MICCAI paper, we present a latent variable model designed to model complex heart structures and to precisely capture the intricate relationships between heart components.
https://t.co/NK4RPZgSsK
#deeplearning #MedicalImaging