Temel Kavramlar
The paper introduces a novel Data Fusion for Data Mining (DF-DM) model that integrates foundational models, embeddings, and best practices from the CRISP-DM process to enable efficient and reliable multimodal data fusion, particularly in complex domains like healthcare.
Özet
The paper proposes a new process model for multimodal data fusion, the Data Fusion for Data Mining (DF-DM) model. The key highlights of the DF-DM model are:
Integration of the CRISP-DM process model: The DF-DM model incorporates the CRISP-DM framework, which emphasizes business understanding, data understanding, and a cyclical process for model refinement. This ensures the model is aligned with practical, real-world applications.
Leveraging foundational models and embeddings: The DF-DM model utilizes foundational models and vector embeddings to reduce the high dimensionality and heterogeneity of multimodal data. This significantly alleviates computational demands and facilitates more effective integration of diverse data types.
Disentangled dense fusion method: The paper introduces a novel "disentangled dense fusion" technique that leverages mutual multimodal embedding information. This method decouples entangled multimodal pairs into compact distinct components: modality-common features and modality-specific knowledge features, reducing inter-modal redundancy while preserving modality-specific expressiveness.
Bias assessment and mitigation: The DF-DM model includes a dedicated level for bias assessment and mitigation, addressing potential biases in data, models, and decision-making.
The efficacy and versatility of the DF-DM model and the disentangled dense fusion method are demonstrated through three healthcare use cases:
Predicting diabetic retinopathy using retinal fundus images and patient metadata
Domestic violence prediction by fusing satellite imagery, internet data, and census data
Identifying clinical and demographic features from radiography images and clinical notes
The results showcase the DF-DM model's potential to significantly impact multimodal data processing, promoting its adoption in diverse, resource-constrained settings.
İstatistikler
The paper does not provide specific numerical data or statistics. The focus is on the proposed DF-DM model and its demonstration through three use cases.
Alıntılar
"The paper introduces a novel Data Fusion for Data Mining (DF-DM) model that integrates foundational models, embeddings, and best practices from the CRISP-DM process to enable efficient and reliable multimodal data fusion, particularly in complex domains like healthcare."
"The DF-DM model utilizes foundational models and vector embeddings to reduce the high dimensionality and heterogeneity of multimodal data. This significantly alleviates computational demands and facilitates more effective integration of diverse data types."
"The paper introduces a novel 'disentangled dense fusion' technique that leverages mutual multimodal embedding information. This method decouples entangled multimodal pairs into compact distinct components: modality-common features and modality-specific knowledge features, reducing inter-modal redundancy while preserving modality-specific expressiveness."