
katherlab
@katherlab
Followers
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Research group "Clinical Artificial Intelligence" @Medizin_TUD @tudresden_de, led by @jnkath
Dresden
Joined July 2023
RT @jnkath: Twitter is really becoming unbearable. I fully support keeping science separate from politics, but this is going too far. Forei….
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RT @jnkath: Happy to share a new review article led by Laura @zigutyte from @Katherlab. We surveyed all contributions at Europe’s largest c….
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RT @jnkath: Today, we hosted the first conference on LLMs in medicine at @katherlab / @tudresden_de, chaired by @IsabellaWies 🤩 We are not….
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RT @jnkath: We had a fun session on artificial intelligence at the @dgho_eV German/Swiss/Austrian conference of #hematology and #oncology i….
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New paper from @katherlab led by @ElNahhasOSM et al. 😊👇.
#NewNProt moves from whole-slide image to #biomarker prediction: end-to-end weakly-supervised #deeplearning in #computationalpathology
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New research from @katherlab led by @IsabellaWies 🤩.
This study developed an open-source tool using a LLM to extract important medical information from clinical text, focusing on decompensated #LiverCirrhosis. The tool identified liver cirrhosis from free text with 100% sensitivity and 96% specificity, and showed strong results
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RT @jnkath: New article from @katherlab in @NatureMedicine: large language models for interoperability in healthcare 👇.
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RT @RPintican: This week was about research. It is inspiring to see how people come together, clinicians with computational scientists, all….
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Exciting new research by @ElNahhasOSM from @katherlab 🤩.
Excited to present my latest work accepted in #MICCAI2024 on joint multi-task learning in computational pathology!. We reach SOTA performance on predicting MSI and HRD status by learning auxiliary regression tasks related to the tumor microenvironment 🔬.
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RT @jnkath: New research from @katherlab, led by @JClusmann : "Prompt Injection Attacks on Large Language Models in Oncology" https://t.co/….
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Just out from @katherlab 👇.
This review explores the transition of deep learning in radiology from laborious fully supervised methods to more scalable weakly supervised methods. @laim_uka @ekfzdigital @jnkath @katherlab @danieltruhn @LeoMisera @FranzesGustav
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New research led by @ChiaraMLL 🤩.
Happy to share a new computational pathology preprint from @katherlab & many other groups: "HIBRID - Histology and ct-DNA based Risk-stratification with Deep Learning". Deep Learning 💻+ ctDNA 🧬= very powerful prognostic model in colorectal cancer ☑️.
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RT @jnkath: New preprint from @katherlab: large language models for end-to-end clincial trial matching. Our system parses study protocols….
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RT @jnkath: Weekend read: We are discussing the future of AI in cancer research in @Cancer_Cell . with @MoritzGe….
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RT @jnkath: New preprint from @katherlab, led by @IsabellaWies - large language models can solve one of the hardest problems in medical inf….
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RT @jnkath: So proud of @IsabellaWies from @tudresden_de and @unimedizinma who is presenting about the “skill of prompt engineering” at our….
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RT @jnkath: What a fun session at @EASLedu @EASLnews #EASLcongress 2024 about "Research in the age of AI" in Milan 🇮🇹🇪🇺. Any biomedical res….
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