DGRUnit: Dual graph reasoning unit for brain tumor segmentation

Ma, Qihang; Zhou, Sanming; Li, Chengye; Liu, Feng; Liu, Yan; Hou, Mingzheng; Zhang, Yi

doi:10.1016/j.compbiomed.2022.106079

Cited by 16 publications

(7 citation statements)

References 24 publications

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“…In healthcare, FL has been used for various tasks, such as disease diagnosis, drug discovery, and medical imaging analysis [47]. Chen et al [48] proposed an FL-based framework for personalized cancer diagnosis that allowed multiple hospitals to collaboratively train a DL model without sharing their patient data. The FL-based method achieved higher accuracy than the traditional centralized learning method while preserving the privacy and security of the data.…”

Section: Federated Learning Applicationsmentioning

confidence: 99%

Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures

Ullah,

Nadeem,

Abrar

et al. 2023

Mathematics

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Brain tumor segmentation in medical imaging is a critical task for diagnosis and treatment while preserving patient data privacy and security. Traditional centralized approaches often encounter obstacles in data sharing due to privacy regulations and security concerns, hindering the development of advanced AI-based medical imaging applications. To overcome these challenges, this study proposes the utilization of federated learning. The proposed framework enables collaborative learning by training the segmentation model on distributed data from multiple medical institutions without sharing raw data. Leveraging the U-Net-based model architecture, renowned for its exceptional performance in semantic segmentation tasks, this study emphasizes the scalability of the proposed approach for large-scale deployment in medical imaging applications. The experimental results showcase the remarkable effectiveness of federated learning, significantly improving specificity to 0.96 and the dice coefficient to 0.89 with the increase in clients from 50 to 100. Furthermore, the proposed approach outperforms existing convolutional neural network (CNN)- and recurrent neural network (RNN)-based methods, achieving higher accuracy, enhanced performance, and increased efficiency. The findings of this research contribute to advancing the field of medical image segmentation while upholding data privacy and security.

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Section: Federated Learning Applicationsmentioning

confidence: 99%

Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures

Ullah,

Nadeem,

Abrar

et al. 2023

Mathematics

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“…Zhang et al [54] used a different modality, distance-based features extracted from limited CT samples, to develop a GNN predictor for pancreatic cystic neoplasm classification; the dataflow followed an established by now scheme -use CNN to generate features and GNN to complete classification. Similarly, Ravinder et al [55] combined CNN and GNN to improve brain tumor type classification using MRI images; whereas Ma et al [56] proposed a dual GCN-GAT architecture for MRI brain tumor segmentation. Yin et al [57] used yet another modality, multi-omics, to demonstrate a superior breast and stomach cancer subtyping accuracy when integrating -omics in a GCN-based predictor.…”

Section: Cancer Classification Subtyping and Gradingmentioning

confidence: 99%

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

Gogoshin,

Rodin

2023

Preprint

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Next-generation cancer and oncology research needs to take full advantage of the multi-modal structured, or graph, information, with the graph datatypes ranging from molecular structures to spatially resolved imaging and digital pathology to biological networks to knowledge graphs. Graph Neural Networks (GNNs) efficiently combine the graph structure representations with the high predictive performance of deep learning, especially on the large multi-modal datasets. In this review article, we survey the landscape of recent (2020-present) GNN applications in the context of cancer and oncology research, and delineate six currently predominant research areas. Subsequently, we identify the most promising directions for future research. We compare GNNs with graphical models and "non-structured" deep learning, and devise the guidelines for cancer and oncology researchers or physician-scientists asking the question of whether they should adopt the GNN methodology in their research pipelines.

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“…In recent years, deep learning models, such as those proposed by Ma et al. ( 6 ), have achieved significant success in automatic brain tumor segmentation. These models excel at capturing both local and global contextual features, but often struggle with vanishing gradients and overfitting, especially in deeper network layers.…”

Section: Introductionmentioning

confidence: 99%

A continuous learning approach to brain tumor segmentation: integrating multi-scale spatial distillation and pseudo-labeling strategies

Li,

Ye,

Huang

et al. 2024

Front. Oncol.

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IntroductionThis study presents a novel continuous learning framework tailored for brain tumour segmentation, addressing a critical step in both diagnosis and treatment planning. This framework addresses common challenges in brain tumour segmentation, such as computational complexity, limited generalisability, and the extensive need for manual annotation.MethodsOur approach uniquely combines multi-scale spatial distillation with pseudo-labelling strategies, exploiting the coordinated capabilities of the ResNet18 and DeepLabV3+ network architectures. This integration enhances feature extraction and efficiently manages model size, promoting accurate and fast segmentation. To mitigate the problem of catastrophic forgetting during model training, our methodology incorporates a multi-scale spatial distillation scheme. This scheme is essential for maintaining model diversity and preserving knowledge from previous training phases. In addition, a confidence-based pseudo-labelling technique is employed, allowing the model to self-improve based on its predictions and ensuring a balanced treatment of data categories.ResultsThe effectiveness of our framework has been evaluated on three publicly available datasets (BraTS2019, BraTS2020, BraTS2021) and one proprietary dataset (BraTS_FAHZU) using performance metrics such as Dice coefficient, sensitivity, specificity and Hausdorff95 distance. The results consistently show competitive performance against other state-of-the-art segmentation techniques, demonstrating improved accuracy and efficiency.DiscussionThis advance has significant implications for the field of medical image segmentation. Our code is freely available at https://github.com/smallboy-code/A-brain-tumor-segmentation-frameworkusing-continual-learning.

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DGRUnit: Dual graph reasoning unit for brain tumor segmentation

Cited by 16 publications

References 24 publications

Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures

Enhancing Brain Tumor Segmentation Accuracy through Scalable Federated Learning with Advanced Data Privacy and Security Measures

Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends

A continuous learning approach to brain tumor segmentation: integrating multi-scale spatial distillation and pseudo-labeling strategies

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