Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity and can only handle graphs with at most thousands of nodes. To this ...

Objectives To assess the current state-of-the-art in deep learning methods applied to pre-operative radiologic staging of colorectal cancer lymph node metastasis. Specifically, by evaluating the data, ...

Abstract: Graph neural networks (GNNs) are capable of modeling graph data using various types of nodes and edges, and thus can be widely used in the fields of recommender systems and bioinformatics.

The scheduled tasks feature (#3) needs a way for users to view and edit workflow recipes. Instead of building a custom workflow editor in our frontend, the workflow editor could be an MCP app — an ...

GPU training is automatic if CUDA is available. CPU and Apple MPS are also supported.