beitrag_KI-krebs

Deep Fake – Mit künstlicher Intelligenz Bilder erstellen


DI Dr. Michael Gadermayr, B.Eng.
Senior Lecturer, Informationstechnik & System-Management, Wirtschaftsinformatik & Digitale Transformation
„Deep Fake“ ist der Ausdruck für eine Technik, die es ermöglicht, mit Hilfe von künstlicher Intelligenz täuschend echt wirkende Bilder oder Videos zu erstellen. So können wir mit freiem Auge nicht mehr erkennen, ob beispielsweise ein Foto eine reale Person zeigt, oder ob diese Abbildung aus verschiedenen Bildern mit dem Computer erzeugt wurde. Dafür sind „nur“ leistungsfähige Grafikkarten und künstliche neuronale Netzwerke, die jedem als Open-Source-Software zur Verfügung stehen, notwendig. Wie diese Methode für die verbesserte Diagnose von Erkrankungen im medizinischen Bereich verwendet werden kann, erforscht Michael Gadermayr, Senior Lecturer am Studiengang Informationstechnik & System-Management der FH Salzburg.
DI Dr. Michael Gadermayr, B.Eng Foto: FH Salzburg

DI Dr. Michael Gadermayr, B.Eng
Foto: FH Salzburg

„Bei der Deep-Fake-Methode agieren künstliche neuronale Netzwerke ähnlich wie unser Gehirn, wobei hier allerdings Algorithmen die genaue Vorgehensweise vorgeben. Das kann man sich in etwa so vorstellen, dass ein Algorithmus aus Bildern anderer Menschen ein neues Foto erzeugt“, erklärt Michael Gadermayr. Dabei werden gewisse Merkmale von einem Menschen genommen und mit Besonderheiten – wie den Augen – eines anderen kombiniert.
In der Open-Source-Software sind die Algorithmen prinzipiell allgemein gehalten, durch Training mit spezifischen Trainingsdaten können sie jedoch angepasst werden. Gadermayr: „Anpassung bedeutet hier, dass Millionen von Parametern in einem Optimierungsverfahren immer wieder verbessert werden. Somit entscheiden größtenteils die Trainingsdaten, ob man mittels eines Netzwerks Kunstwerke oder beispielsweise Fakevideos von Politikern erschafft.“

Deep-Fake zur Verbesserung von Diagnosen in der Medizin
Auf der Deep-Fake-Methode basiert die wissenschaftliche Arbeit des Senior Lecturers und seinen Kooperationspartnern von der RWTH Aachen. Konkret fokussiert sich ihre Forschung auf die automatisierte Verarbeitung von Bilddaten in der digitalen Pathologie. „Für die Diagnostik, beispielsweise von Krebserkrankungen, fallen immense Datenmengen an. Die mikroskopischen Bilder besitzen zum Teil Auflösungen im Bereich von bis zu mehreren Gigapixeln“, so der gebürtige Oberalmer. Da eine gründliche manuelle Analyse dieser Daten somit häufig entweder extrem zeitaufwendig oder einfach nicht machbar wäre, ist die Datenvorverarbeitung ein äußerst wichtiges Gebiet. Durch Detektion, Segmentierung und Vermessung von relevanten Gewebebereichen kann beispielsweise eine datengetriebene Analyse effizient durchgeführt werden. Moderne Netzwerk-Architekturen ermöglichen außerdem eine direkte Klassifikation von pathologischen Schnittbildern in unterschiedliche Erkrankungstypen. Das neuronale Netz fungiert dabei als Blackbox und erstellt selbständig eine Diagnose ohne jegliche menschliche Interaktion.

Foto: FH Salzburg/Pixabay

Bildvariation wird mit der Deep-Fake-Technologie minimiert
Eine große Hürde in der digitalen Pathologie stellt die Variabilität der Daten dar. „Starke visuelle Unterschiede entstehen durch uneinheitliche Scanning-Methoden, verschiedene Färbungen der Bilder sowie unterschiedliche Pathologien“, weiß Gadermayr. Während Machine Learning ein ideales Werkzeug ist, um wiederkehrende Muster zu erlernen, stellt Variabilität zwischen Trainingsdaten und Testdaten immer noch ein großes Problem dar. Im Vergleich zum Menschen können Maschinen sogenannte „Domain-Shifts“ (strukturelle Unterschiede zwischen Trainings- und Testdaten) nicht durch „natürliche Intelligenz“ ausgleichen

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„Wir fokussieren uns darauf, die Daten weitgehend zu normalisieren, sodass maschinelle Methoden, beispielsweise zur Segmentierung, einheitliches Material vorfinden. Zu diesem Zweck bedienen wir uns derselben Technologie wie Deep Fake.“ Künstliche neuronale Netze werden trainiert, um aus beliebigen Färbungen eine einheitliche Standard-Färbung zu generieren, die schlussendlich verarbeitet werden kann. Dies geschieht mittels sogenannter Adversarial Networks, die in einem gegnerischen Setting optimiert werden. „Generator“-Netze versuchen, bestmöglich Daten von einer in eine andere Färbung umzuwandeln und werden dafür belohnt, wenn die generierten Bilder von sogenannten „Diskriminator“-Netzen nicht von echten unterschieden werden können. Diese Diskriminator-Netze werden belohnt, wenn sie die Unterscheidung korrekt durchführen. Basierend auf der Belohnung bzw. Bestrafung werden die Netze iterativ trainiert. Die final erzeugten virtuellen pathologischen Schnittbilder sehen täuschend echt aus und können im Weiteren wie Originalbilder verarbeitet werden.

Forschung in der biomedizinischen Bildanalyse
In der aktuellen Forschung verwenden die Forschenden die Deep-Fake-Methodologie nicht nur, um Variabilität auszugleichen [1,2], sondern auch um die Segmentierungsgenauigkeit zu verbessern [3]. „Dafür wird für jedes Bild eine Reihe virtueller Kopien erzeugt, welche die Originalbilder unter anderen Färbungen zeigen. Die virtuellen Bilder werden dann mit den Originaldaten kombiniert, sodass der Informationsgehalt vergrößert wird“, erklärt Gadermayr. Das neuronale Netz, das die Segmentierung durchführen soll, wird nun entsprechend auf eine größere Datenbasis angewendet, wodurch die Genauigkeit gesteigert werden kann.


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