Moving Beyond 2D: 3D CT AI with Ibrahim Ethem Hamamci
Artikel konnten nicht hinzugefügt werden
Der Titel konnte nicht zum Warenkorb hinzugefügt werden.
Der Titel konnte nicht zum Merkzettel hinzugefügt werden.
„Von Wunschzettel entfernen“ fehlgeschlagen.
„Podcast folgen“ fehlgeschlagen
„Podcast nicht mehr folgen“ fehlgeschlagen
Moving Beyond 2D: 3D CT AI with Ibrahim Ethem Hamamci
-
Gesprochen von:
-
Von:
Über diesen Titel
In this episode, I sit down with Dr. Ibrahim Ethem Hamamci to unpack what it really takes to build modern AI for 3D CT—covering multimodal learning (images + text), the practical realities of clinical deployment, and why “moving beyond 2D” is such a big shift in radiology AI.
We dive into CT-RATE, a large-scale dataset of chest CT volumes paired with radiology reports (plus labels/metadata), why it’s been such a catalyst for 3D vision–language research, and what it enables for training foundation-style models in radiology. The dataset is publicly available on Hugging Face here: https://huggingface.co/datasets/ibrahimhamamci/CT-RATE
If you want a clear primer on core concepts in AI for radiology—representation learning, multimodal supervision and what changes when you go from 2D images to 3D volumes—this conversation is a great starting point. We also dig into why a lot of work still needs to be done: models need to be more robust across scanners and sites, evaluation has to go beyond headline metrics, and real clinical use demands reliability, workflow fit, and accountability.
