Search Results for author:
Found 7 papers, 3 papers with code
Physics-Inspired Degradation Models for Hyperspectral Image Fusion
To fill this gap, we propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI, which comprises a spatial degradation network (SpaDN) and a spectral degradation network (SpeDN).
AdaMedGraph: Adaboosting Graph Neural Networks for Personalized Medicine
However, selecting the appropriate edge feature to define patient similarity and construct the graph is challenging, given that each patient is depicted by high-dimensional features from diverse sources.
Auto robust relative radiometric normalization via latent change noise modelling
Relative radiometric normalization(RRN) of different satellite images of the same terrain is necessary for change detection, object classification/segmentation, and map-making tasks.
Automatically eliminating seam lines with Poisson editing in complex relative radiometric normalization mosaicking scenarios
Relative radiometric normalization (RRN) mosaicking among multiple remote sensing images is crucial for the downstream tasks, including map-making, image recognition, semantic segmentation, and change detection.
A Structure-Aware Relation Network for Thoracic Diseases Detection and Segmentation
Leveraging on constant structure and disease relations extracted from domain knowledge, we propose a structure-aware relation network (SAR-Net) extending Mask R-CNN.
The Detection of Thoracic Abnormalities ChestX-Det10 Challenge Results
The detection of thoracic abnormalities challenge is organized by the Deepwise AI Lab.
ChestX-Det10: Chest X-ray Dataset on Detection of Thoracic Abnormalities
Instance level detection of thoracic diseases or abnormalities are crucial for automatic diagnosis in chest X-ray images.
