The paper discusses the use of deep neural networks for integrating diverse data types in oncology settings, such as radiology, pathology, genomics, proteomics, and clinical records. It highlights the potential of deep learning frameworks like Graph Neural Networks (GNNs) and Transformers in multimodal data fusion .
The review article provides an in-depth analysis of the state-of-the-art in GNNs and Transformers for multimodal data fusion in oncology, highlighting notable research studies and their findings .
It also discusses the foundations of multimodal learning, inherent challenges, and opportunities for integrative learning in oncology
The paper aims to demonstrate the promising role that multimodal neural networks can play in cancer prevention, early diagnosis, and treatment
The paper mentions that early fusion and cross-attention are two approaches to combine multiple modalities in deep learning models . Early fusion involves concatenating the data from different modalities into a single input before processing it with Transformer layers . Cross-attention allows the model to selectively attend to different modalities based on their relevance to the task and capture complex interactions between modalities.
The paper also highlights the challenges of developing models that can generalize across different cancer sites and the need for improved universality of ML models.
It mentions the use of federated learning to train large multimodal models using local data without sharing it with other sites, while still benefiting from data from other sites.
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The paper discusses the use of deep neural networks for integrating diverse data types in oncology settings, such as radiology, pathology, genomics, proteomics, and clinical records. It highlights the potential of deep learning frameworks like Graph Neural Networks (GNNs) and Transformers in multimodal data fusion . The review article provides an in-depth analysis of the state-of-the-art in GNNs and Transformers for multimodal data fusion in oncology, highlighting notable research studies and their findings . It also discusses the foundations of multimodal learning, inherent challenges, and opportunities for integrative learning in oncology
The paper aims to demonstrate the promising role that multimodal neural networks can play in cancer prevention, early diagnosis, and treatment
The paper mentions that early fusion and cross-attention are two approaches to combine multiple modalities in deep learning models . Early fusion involves concatenating the data from different modalities into a single input before processing it with Transformer layers . Cross-attention allows the model to selectively attend to different modalities based on their relevance to the task and capture complex interactions between modalities. The paper also highlights the challenges of developing models that can generalize across different cancer sites and the need for improved universality of ML models. It mentions the use of federated learning to train large multimodal models using local data without sharing it with other sites, while still benefiting from data from other sites.
Cite
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks- A Review.pdf
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