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Found 3 papers, 2 papers with code
Building Flexible, Scalable, and Machine Learning-ready Multimodal Oncology Datasets
The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data.
Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review
Recent deep learning frameworks such as Graph Neural Networks (GNNs) and Transformers have shown remarkable success in multimodal learning.
Exploring Robust Architectures for Deep Artificial Neural Networks
The architectures of deep artificial neural networks (DANNs) are routinely studied to improve their predictive performance.
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