Ask a radiology resident to find the spleen on a single unfamiliar scan after seeing only one labeled example beforehand and you are asking for exactly the problem this paper takes on, except the resident here is a neural network.…
Prototype Guided Graph Reasoning for Few Shot Medical Segmentation Read More »
Picture a strip of land seen through radar instead of a camera. A river cuts a clean line through farmland, a cluster of buildings sits in a blocky grid, and a forest spreads out as a grainy, speckled mess. To…
Manifold Aware Fusion for PolSAR Image Classification Read More »
A team at Boston University built GTP — a Graph-Transformer for Pathology that fuses graph convolutional networks with vision transformers to classify gigapixel whole slide images of lung cancer at 91.2% accuracy, plus a novel GraphCAM technique that highlights exactly…
GTP: The Graph-Transformer That Reads Whole Slide Pathology Images Like a Pathologist Read More »
A team at Chongqing University built a graph neural network that embeds multi-sensor degradation data into hyperbolic space — where the geometry naturally fits hierarchical structures — and dynamically adapts the curvature of that space during training to match the…
DCPGCN: Dynamic Curvature Pooling in Hyperbolic Space for Multi-Sensor RUL Prediction Read More »
Recommender systems built on metric learning represent each user and item as a point in a shared space and push the distance between a user and the items they like to shrink relative to items they do not like. This…
BIM-CGN: Causal Graph Attention Fixes Metric Learning Recommenders Read More »