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

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

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...

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Bibliographic Details
Main Authors: Noriaki Hashimoto, Hiroyuki Hanada, Hiroaki Miyoshi, Miharu Nagaishi, Kensaku Sato, Hidekata Hontani, Koichi Ohshima, Ichiro Takeuchi
Format: Article
Language:English
Published: Elsevier 2024-12-01
Series:Journal of Pathology Informatics
Subjects:
Digital pathology
Mixture of experts
Multiple instance learning
Whole slide image
Flow cytometry
Online Access:http://www.sciencedirect.com/science/article/pii/S2153353923001736
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