Multimodal Fusion
Multimodal Fusion - Related Videos
02:45
Nuclear Fusion
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2020
The process of converting very light nuclei into heavier nuclei is also accompanied by the conversion of mass into large amounts of energy, a process called fusion. The principal source of energy in the sun is a net fusion reaction in which four hydrogen nuclei fuse and ultimately produce one helium nucleus and two positrons. A helium nucleus has a mass that is 0.7% less than that of four hydrogen nuclei; this lost mass is converted into energy during the fusion. This reaction produces about...
09:19
Analysis of SNARE-mediated Membrane Fusion Using an Enzymatic Cell Fusion Assay
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2012
We have developed a cell fusion assay that quantifies SNARE-mediated membrane fusion events by activated expression of β-galactosidase.
06:10
Live Imaging of Mouse Secondary Palate Fusion
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Cited by 11 •
2017
Here, we present a protocol for live imaging of mouse secondary palate fusion using confocal microscopy. This protocol can be used in combination with a variety of fluorescent reporter mouse lines, and with pathway inhibitors for mechanistic insight. This protocol can be adapted for live imaging in other developmental systems.
09:27
Generation of Fluorescent Protein Fusions in Candida Species
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Cited by 6 •
2017
PCR-mediated gene modification can be used to generate fluorescent protein fusions in Candida species, which facilitates visualization and quantitation of yeast cells and proteins. Herein, we present a strategy for constructing a fluorescent protein fusion (Eno1-FP) in Candida parapsilosis.
04:58
Method of Studying Palatal Fusion using Static Organ Culture
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Cited by 6 •
2015
Studies of palate development are motivated by the incidence of cleft palate, a birth defect that imposes a tremendous health burden and can leave lasting disfigurement. We demonstrate here a technique to culture palatal shelves that can be used to study different signaling pathways involved in palatal development and fusion.
FAQs
Early fusion combines raw or engineered measurements before modeling, allowing the system to analyze modalities jointly from the outset. Intermediate fusion first transforms each modality into a learned representation, then combines those representations. Late fusion keeps modality-specific predictions separate until the final stage. These choices determine where information is integrated and how modality-specific structure contributes to the result.
Medical images, clinical records, laboratory measurements, and genomic profiles encode information in different scales and formats. Alignment and compatible feature handling are therefore necessary before their information can be combined meaningfully. Without this step, relationships among modalities may be difficult to analyze, limiting the ability to connect anatomical, biological, and clinical signals in one assessment.
Missing information is a central consideration because patients may not have every modality available, such as imaging, laboratory data, clinical records, or genomic profiles. Fusion strategies must address these gaps when aligning and combining modality-specific information. Handling incomplete inputs helps preserve the usefulness of the overall analysis for diagnosis, prognosis, treatment selection, or monitoring.
Each modality can describe a different aspect of a patient's condition. Medical images contribute anatomical information, genomic profiles and laboratory measurements provide biological signals, and clinical records add patient-related context. Combining these complementary perspectives can support a more complete analysis than considering one source alone, strengthening applications that depend on linked evidence across disease and patient characteristics.
A typical workflow begins by identifying relevant sources, such as images, clinical records, laboratory measurements, or genomic profiles. The information is then aligned despite differences in scale and format, followed by early, intermediate, or late integration. The combined analysis can subsequently support disease detection, diagnosis, prognosis, treatment selection, or patient monitoring.
Multimodal fusion is especially relevant when no single source captures the full clinical picture. Linking imaging with records, laboratory measurements, or genomic profiles can provide information spanning anatomy, biology, and patient context. This broader evidence base is useful for disease detection and diagnosis, as well as for estimating prognosis, selecting treatments, and monitoring patients over time.
By connecting patient-specific clinical information with anatomical, laboratory, and genomic signals, multimodal fusion can support decisions tailored to an individual's disease profile and observed condition. Its outputs may inform diagnosis, prognosis, treatment selection, and ongoing monitoring. In this way, the approach links diverse evidence to clinical decision-making and advances more personalized healthcare.