Instance importance-Aware graph convolutional network for 3D medical diagnosis.
Med Image Anal
; 78: 102421, 2022 05.
Article
in English
| MEDLINE | ID: covidwho-1747693
ABSTRACT
Automatic diagnosis of 3D medical data is a significant goal of intelligent healthcare. By exploiting the abundant pathological information of 3D data, human experts and algorithms can provide accurate predictions for patients. Considering the high cost of collecting exhaustive annotations for 3D data, a sustainable alternative is to develop diagnosis algorithms with merely patient-level labels. Motivated by the fact that 2D slices of 3D data hold explicit diagnostic efficacy, we propose the Instance Importance-aware Graph Convolutional Network (I2GCN) under the multi-instance learning (MIL). Specifically, we first calculate the instance importance of each slice towards diagnosis using a preliminary MIL classifier, which is further utilized to promote the refined diagnosis branch. In the refined diagnosis branch, we devise the Instance Importance-aware Graph Convolutional Layer (I2GCLayer) to exploit complementary features in both importance-based and feature-based topologies. Moreover, to alleviate the deficient supervision of 3D dataset, we propose the importance-based Sub-Graph Augmentation (SGA) to effectively regularize the framework training. Extensive experiments confirm the effectiveness of our method with different organs and modals on the CC-CCII and PROSTATEx datasets, which outperforms state-of-the-art methods by a large margin. The source code is available at https//github.com/CityU-AIM-Group/I2GCN.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Algorithms
/
Neural Networks, Computer
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Med Image Anal
Journal subject:
Diagnostic Imaging
Year:
2022
Document Type:
Article
Affiliation country:
J.media.2022.102421
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