Add to ORKGDue to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, etc., massive amounts of data are generated on a daily basis. While massive biomedical data sets yield more information about pathologies, they also present new challenges of how to fully explore the data. Data fusion methods are a step forward towards a better understanding of data by bringing multiple data observations together to increase the consistency of the information. However, data generation is merely the first step, and there are many other factors involved in the fusion process like noise, missing data, data scarcity, and high dimensionality. In this paper, an overview of the ...
Abstract Advancements in deep learning techniques carry the potential to make significant contributi...
Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visual...
The medical image fusion is the process of coalescing multiple images from multiple imaging modaliti...
Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Comput...
Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Comput...
Data fusion aims to provide a more accurate description of a sample than any one source of data alon...
The papers in this special section examine important current topics on multimodal data fusion in the...
International audienceThe accumulation of several data coming from medical images and signals, exper...
90 p. : ill. ; 30 cmDuring recent years, medical imaging examinations more and more often use inform...
In image-based medical decision-making, different modalities of medical images of a given organ of a...
Data registration, i.e., the process of transforming a dataset so that the entities represented are ...
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex rela...
This book examines the principles and applications of biomedical imaging and signals processing as w...
Abstract Background With a wid...
International audienceThe collection of various data coming from anatomical and functional imagery i...
Abstract Advancements in deep learning techniques carry the potential to make significant contributi...
Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visual...
The medical image fusion is the process of coalescing multiple images from multiple imaging modaliti...
Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Comput...
Due to the proliferation of biomedical imaging modalities, such as Photo-acoustic Tomography, Comput...
Data fusion aims to provide a more accurate description of a sample than any one source of data alon...
The papers in this special section examine important current topics on multimodal data fusion in the...
International audienceThe accumulation of several data coming from medical images and signals, exper...
90 p. : ill. ; 30 cmDuring recent years, medical imaging examinations more and more often use inform...
In image-based medical decision-making, different modalities of medical images of a given organ of a...
Data registration, i.e., the process of transforming a dataset so that the entities represented are ...
Biomedical data are becoming increasingly multimodal and thereby capture the underlying complex rela...
This book examines the principles and applications of biomedical imaging and signals processing as w...
Abstract Background With a wid...
International audienceThe collection of various data coming from anatomical and functional imagery i...
Abstract Advancements in deep learning techniques carry the potential to make significant contributi...
Data fusion can be used to combine multiple data sources or modalities to facilitate enhanced visual...
The medical image fusion is the process of coalescing multiple images from multiple imaging modaliti...