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Ready for #biapol public viewing on the DKFZ webinar on metrics for image analysis! 😃 Mara Lampert Marcelo Zoccoler Laura Žigutytė

I wrote a FocalPlane blog post about "Rescaling images and voxel (an)isotropy" adressing the importance of rescaling 3D image data on an example dataset provided by B. Duygu Özpolat, Ph.D. (CC-BY). I hope it's useful🔬🖥️ Feedback ❤️ly welcome! focalplane.biologists.com/2023/03/02/res…

#BiAPoL Napari #hackathon time Physics of Life Dresden 😃 (actually still at Center for Systems Biology Dresden 😁)

napari-skimage-regionprops allows you to: - interactively get objects size, shape✅ "But I want to relate to objects from other channels!" You can write python code to do that. OR... I am introducing multichannel summary statistics to github.com/haesleinhuepf/…🚀🙂give it a try!

I wrote a FocalPlane blog post about "Annotating 3D images in napari" adressing time-efficient ground truth generation using an example dataset provided by B. Duygu Özpolat, Ph.D. (CC-BY). I hope it's useful🔬🖥️ Feedback ❤️ly welcome! focalplane.biologists.com/2023/03/30/ann…

My first paper ever is now available as a preprint on ArXiv!

The napari-clusters-plotter hit 1000 code commits 🎂🥳 and now comes with histograms for even easier data exploration 🔬🖥️📊 Fantastic community / team work by Thorsten Wagner Mara Lampert Laura Žigutytė Ryan Savill and Marcelo Zoccoler #DataScience 🚀 napari-hub.org/plugins/napari…

So proud of my research group "Clinical AI" at Else Kröner Fresenius Center for Digital Health TU Dresden @Medizin_TUD - the team has grown again and we have big plans for the next years! jnkather.github.io/team/ - Photos by SECAI: School of Embedded Composite AI :-)

Guess how happy it makes to see others incorporating your napari-plugin into their napari-plugin 🔬➡️🖥️➡️🖥️➡️🥰 Laura Žigutytė Ryan Savill Marcelo Zoccoler 🙌

Looking forward to presenting at the Cancer Seminar series on May 16th, hosted by Linköpings universitet. I will be sharing how the Kather Lab uses AI to predict complex biomarkers from routine histopathology slides in a step-by-step, layperson guide! Else Kröner Fresenius Center for Digital Health TU Dresden

It was an honor to host Prof. Junya Fukuoka and Prof. Andrey Bychkov from Kameda Medical Center🇯🇵 at Else Kröner Fresenius Center for Digital Health TU Dresden @Medizin_TUD yesterday 🇪🇺 We learned a lot about bringing #AI to daily practice in digital #pathology

Liver researchers beyond borders! 🌍 Grateful for #EASLCongress for connecting like-minded people #socialmediaworkshop #HappeningNow #LiverTwitter

Fantastic way to end the first day of #EASLCongress! Many thanks for inviting me, and fingers crossed for many succesfull future collaborations between computational researchers and physicians 🤩

🚨#SaveTheDate 🚨 Jois us May 13th-15th 2024: #BioImageAnalysis + #DataScience + #Python Training School ScaDS.AI.AI in Leipzig! 🔬🖥️🚀 Registration opens soon™️

New content online: Artificial intelligence in liver cancer — new tools for research and patient management dlvr.it/T5b5cN

We had a fantastic #BioImageAnalysis+#DataScience training school last week! Big thanks to the trainers Anja Neumann, Christian Martin, Dušan Praščević, Jan Ewald, Laura Žigutytė, Marie-Sophie von Braun and Matthias Täschner, and the trainees for the amazing atmosphere 🔬🖥️🚀

Our Featured article this month by Jakob Nikolas Kather & colleagues focuses on AI in #livercancer, providing insights into new tools for research and patient management nature.com/articles/s4157… Free to read: rdcu.be/dPZEW

Finally out in Nature Protocols : our group's workflows for end to end computational pathology. Compatible with UNI and other foundation models. Led by Omar S.M. El Nahhas from katherlab journal link: nature.com/articles/s4159… full text: rdcu.be/dT35l

Happy to share a new review article led by Laura Laura Žigutytė from @Katherlab. We surveyed all contributions at Europe’s largest conference on hepatology, EASLnews 2024. The liver research field is already integrating AI techniques for diagnostics, evaluating treatment

Deep Learning for Biomarker Discovery in Cancer Genomes 1. This study introduces a novel deep learning framework for identifying clinically relevant biomarkers—Microsatellite Instability (MSI) and Homologous Recombination Deficiency (HRD)—directly from somatic mutation data in