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

• Päätekijät: Noriaki Hashimoto (Tekijä), Hiroyuki Hanada (Tekijä), Hiroaki Miyoshi (Tekijä), Miharu Nagaishi (Tekijä), Kensaku Sato (Tekijä), Hidekata Hontani (Tekijä), Koichi Ohshima (Tekijä), Ichiro Takeuchi (Tekijä)
• Aineistotyyppi: Kirja
• Julkaistu: Elsevier, 2024-12-01T00:00:00Z.
• Aiheet: article
• Linkit: 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...