DAE-GCN: Identifying Disease-Related Features for Disease Prediction

Wang, Churan; Sun, Xinwei; Zhang, Fandong; Yu, Yizhou; Wang, Yizhou

doi:10.1007/978-3-030-87240-3_5

Cited by 3 publications

(1 citation statement)

References 8 publications

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“…There are many challenging and interesting questions to pursue. For example, treating survival prediction as a regression problem instead of binary classification [13], replacing radiomic features with deep learning features [2], incorporating clinical variables into outcome predictions [18]. We believe the resources we have developed and outlined in this study will aid research in these directions and have implications for patient management.…”

Section: Discussionmentioning

confidence: 99%

Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

Chen¹,

Martel²

2022

Preprint

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Cancer stage is a large determinant of patient prognosis and management in many cancer types, and is often assessed using medical imaging modalities, such as CT and MRI. These medical images contain rich information that can be explored to stratify patients within each stage group to further improve prognostic algorithms. Although the majority of cancer deaths result from metastatic and multifocal disease, building imaging biomarkers for patients with multiple tumors has been a challenging task due to the lack of annotated datasets and standard study framework. In this paper, we process two public datasets to set up a benchmark cohort of 341 patient in total for studying outcome prediction of multifocal metastatic cancer. We identify the lack of expressiveness in common multiple instance classification networks and propose two injective multiple instance pooling functions that are better suited to outcome prediction. Our results show that multiple instance learning with injective pooling functions can achieve state-of-the-art performance in the non-small-cell lung cancer CT and head and neck CT outcome prediction benchmarking tasks. We will release the processed multifocal datasets, our code and the intermediate files i.e. extracted radiomic features to support further transparent and reproducible research.

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Section: Discussionmentioning

confidence: 99%

Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

Chen¹,

Martel²

2022

Preprint

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A Novel Opportunistic Access Algorithm Based on GCN Network in Internet of Mobile Things

Cai

Sheng

Wang

et al. 2023

IEEE Internet Things J.

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Medical image processing using graph convolutional networks: A review

Liu,

Zhu,

et al. 2023

PPM

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Deep learning, especially graph convolutional networks (GCNs), has been widely applied in various scenarios. Particularly in the field of medical image processing, the research on GCNs have continued to make breakthroughs and has been successfully applied to various tasks, such as medical image segmentation, as well as disease detection, localization, classification and diagnosis. GCNs have demonstrated the capacity to autonomously learn latent disease features from vast medical image datasets. Their potential value and enhanced capabilities in prediction, analysis, and decision-making in perioperative medical imaging have become evident. In recent years, GCNs have rapidly emerged as a research focus in the realm of medical image analysis. First, this review provides a concise overview of the development from convolutional neural networks to GCNs, delineating their algorithmic foundations and network structures. Subsequently, the diverse applications of GCNs in perioperative medical image processing are extensively reviewed, including medical image segmentation, image reconstruction, disease prediction, lesion detection and localization, disease classification and diagnosis, and surgical intervention. Finally, this review discusses the prevailing challenges and offers insights into future research directions for the utilization of GCN methods in the medical field.

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DAE-GCN: Identifying Disease-Related Features for Disease Prediction

Cited by 3 publications

References 8 publications

Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

Metastatic Cancer Outcome Prediction with Injective Multiple Instance Pooling

A Novel Opportunistic Access Algorithm Based on GCN Network in Internet of Mobile Things

Medical image processing using graph convolutional networks: A review

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