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

• Main Authors: Noriaki Hashimoto (Author), Hiroyuki Hanada (Author), Hiroaki Miyoshi (Author), Miharu Nagaishi (Author), Kensaku Sato (Author), Hidekata Hontani (Author), Koichi Ohshima (Author), Ichiro Takeuchi (Author)
• Format: Book
• Published: Elsevier, 2024-12-01T00:00:00Z.
• Subjects: article
• Online Access: 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...