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

• Κύριοι συγγραφείς: Noriaki Hashimoto (Συγγραφέας), Hiroyuki Hanada (Συγγραφέας), Hiroaki Miyoshi (Συγγραφέας), Miharu Nagaishi (Συγγραφέας), Kensaku Sato (Συγγραφέας), Hidekata Hontani (Συγγραφέας), Koichi Ohshima (Συγγραφέας), Ichiro Takeuchi (Συγγραφέας)
• Μορφή: Βιβλίο
• Έκδοση: Elsevier, 2024-12-01T00:00:00Z.
• Θέματα: article
• Διαθέσιμο 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...