Johannes Lotz
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johannes.lotz.dev
Johannes Lotz
@johannes.lotz.dev
Scientist in Computational Pathology @FraunhoferMEVIS / @uniluebeck.bsky.social, Vorstand im KIKS e.V. in Lübeck.

fedi: @johanneslotz@scholar.social, joha1o@chaos.social

#pathology #computationalpathology
Reposted by Johannes Lotz
Schön auch dazu:

BM Bastian Langbehn (fraktionslos) Änderungsantrag zu - AM Andreas Zander (CDU)

Beschlussvorschlag
In die Liste der Unterstützer:innen werden
Die Unklar.Bar
Die PARTEI Lübeck
mit aufgenommen.

1/2
March 11, 2025 at 1:47 PM
Reposted by Johannes Lotz
In Lübeck stellt die CDU ebenfalls so eine Anfrage und listet u.a. Schüler:innenvertretungen einiger Lübecker Schulen auf. www.luebeck.de/de/rathaus/p...
Politik Informationssystem Lübeck - Rathaus
www.luebeck.de
March 11, 2025 at 6:36 AM
Reposted by Johannes Lotz
To be clear: was hilft gegen Influenza?
1. Impfung

(lange nichts)

(Sehr lange nichts)

Dann irgendwann Maske tragen, desinfizieren und Abstand halten.
Aber -
February 4, 2025 at 7:40 PM
Big shoutout to @tillnitus.bsky.social (who did the lion’s share of the work 🦁), to our co-authors Raphael Schäfer, Henning Höfener, Friedrich Feuerhake, Dorit Merhof, and Fabian Kießling and to the whole team at FraunhoferMEVIS! Special thanks to the reviewers for their constructive feedback!
🧵5/5
January 10, 2025 at 11:44 AM
You can already find the base model on GitHub (github.com/FraunhoferME...), the pathology-specific model will follow shortly.

🧵4/5
GitHub - FraunhoferMEVIS/MedicalMultitaskModeling: Training foundational medical imaging models using multi-task learning.
Training foundational medical imaging models using multi-task learning. - FraunhoferMEVIS/MedicalMultitaskModeling
github.com
January 10, 2025 at 11:44 AM
... CTransPath (by Wang et al., doi.org/10.1016/j.me...) and additionally, we observed similar results when comparing against the larger UNI model (by Chen et al., doi.org/10.1038/s415..., see the appendix for more details).

🧵3/5
January 10, 2025 at 11:44 AM
“Tissue Concepts” is a resource-efficient foundation model for pathology. We trained with only 6% of the data compared to other (self-supervised) models, and it maintained great performance.

It outperforms self-supervised learning when the model is of similar size (we compared to ...
🧵2/5
January 10, 2025 at 11:43 AM