{"id":49927,"date":"2025-05-20T17:26:47","date_gmt":"2025-05-20T15:26:47","guid":{"rendered":"https:\/\/www.h-its.org\/de\/?post_type=hits-project&p=49927"},"modified":"2025-05-27T11:13:45","modified_gmt":"2025-05-27T09:13:45","slug":"deepsam","status":"publish","type":"hits-project","link":"https:\/\/www.h-its.org\/de\/projects\/deepsam\/","title":{"rendered":"DeepSAM: Machine Learning under Symmetries with Applications in Astronomy and Medicine"},"content":{"rendered":"
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\"\"<\/a>
Bild erstellt mit Mistral AI<\/figcaption><\/figure>\n<\/div>\n\n\n

Viele Wissenschaftsfelder stehen, wenngleich in sehr unterschiedlichen Gr\u00f6\u00dfenordnungen und Proportionen, vor \u00e4hnlichen Herausforderungen. Ein Beispiel ist die sachgerechte automatisierte Verarbeitung bild\u00e4hnlicher Strukturen, die sowohl Astronomie als auch Medizin vor Probleme stellt. Aufgrund der enormen Menge verf\u00fcgbarer Daten sind Deep-Learning-Methoden pr\u00e4destiniert f\u00fcr diese Aufgabe. Obwohl hier bereits gro\u00dfe Fortschritte erzielt wurden, gelingt es meist immer noch nicht, die Symmetrien der zugrunde liegenden Systeme optimal zu nutzen. Beispielsweise hat die Ausrichtung biologischer Zellen und Galaxien keine inh\u00e4rente Bedeutung und sollte daher beim Aufbau geeigneter Machine-Learning-Modelle nicht ber\u00fccksichtigt werden.<\/p>\n\n\n\n

In diesem Projekt entwickeln wir neuartige Deep-Learning-Methoden, die diese Probleme so behandeln, dass sie die Komplexit\u00e4t der Daten ber\u00fccksichtigen und gleichzeitig eine f\u00fcr die Naturwissenschaften entscheidende Interpretierbarkeit erm\u00f6glichen. Wir beabsichtigen, diese Methoden sowohl in der Astronomie als auch in der Medizin einzusetzen.<\/p>\n\n\n\n

Beteiligte: <\/strong>
Kai Polsterer<\/a> (AIN)
Vincent Heuveline<\/a> (DMQ)
<\/p>\n\n\n\n

Mitglieder:<\/strong>
Romain Chazotte<\/a>
<\/p>\n\n\n\n

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