Multi-Center Anatomical Segmentation with Heterogeneous Labels Via Landmark-Based Models
Author:
Affiliation:
1. CONICET-UNL,Research Institute for Signals, Systems and Comp. Intelligence, Sinc(i),Argentina
2. Université Paris-Saclay, Inria Saclay,MICS, CentraleSupélec,France
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10230311/10230322/10230691.pdf?arnumber=10230691
Reference19 articles.
1. Improving Anatomical Plausibility in Medical Image Segmentation via Hybrid Graph Neural Networks: Applications to Chest X-Ray Analysis
2. Hybrid Graph Convolutional Neural Networks for Landmark-Based Anatomical Segmentation
3. PadChest: A large chest x-ray image dataset with multi-label annotated reports
4. Development of a Digital Image Database for Chest Radiographs With and Without a Lung Nodule
5. Learning from Partially Overlapping Labels: Image Segmentation Under Annotation Shift
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