Multimodal Gated Mixture of Experts Using Whole Slide Image and Flow Cytometry for Multiple Instance Learning Classification of Lymphoma

• Główni autorzy: Noriaki Hashimoto (Autor), Hiroyuki Hanada (Autor), Hiroaki Miyoshi (Autor), Miharu Nagaishi (Autor), Kensaku Sato (Autor), Hidekata Hontani (Autor), Koichi Ohshima (Autor), Ichiro Takeuchi (Autor)
• Format: Książka
• Wydane: Elsevier, 2024-12-01T00:00:00Z.
• Hasła przedmiotowe: article
• Dostęp online: Connect to this object online.

In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. In pathological diagnosis of malignant lymphoma, FC...