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A review: Deep learning for medical image segmentation using multi-modality fusion

Array (New York), 2019-09, Vol.3-4, p.100004, Article 100004

2019

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Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism

International journal of imaging systems and technology, 2021-03, Vol.31 (1), p.16-27

2021

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Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions

IEEE transactions on image processing, 2019-02, Vol.28 (2), p.755-766

2019

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Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention

International journal for computer assisted radiology and surgery, 2024-02, Vol.19 (2), p.273-281

2024

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Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

Computerized medical imaging and graphics, 2017-09, Vol.60, p.42-49

2017

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Dissimilarity Metric Learning in the Belief Function Framework

IEEE transactions on fuzzy systems, 2016-12, Vol.24 (6), p.1555-1564

2016

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Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

Journal of imaging, 2023-04, Vol.9 (4), p.81

2023

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Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction

Medical image analysis, 2016-08, Vol.32, p.257-268

2016

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Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions

IEEE transactions on image processing, 2017-07, Vol.26 (7), p.3187-3195

2017

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Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients With PET

IEEE transactions on medical imaging, 2014-04, Vol.33 (4), p.995-1003

2014

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Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier

PloS one, 2017-03, Vol.12 (3), p.e0173208-e0173208

2017

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Semi-automatic lymphoma detection and segmentation using fully conditional random fields

Computerized medical imaging and graphics, 2018-12, Vol.70, p.1-7

2018

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CUS-RF-Based Credit Card Fraud Detection with Imbalanced Data

Journal of risk analysis and crisis response, 2022-09, Vol.12 (3)

2022

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Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

Journal of imaging, 2022-05, Vol.8 (5), p.130

2022

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Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

Computers in biology and medicine, 2020-11, Vol.126, p.104037-104037, Article 104037

2020

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A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction

Entropy (Basel, Switzerland), 2022-03, Vol.24 (4), p.436

2022

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Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities

IEEE transactions on image processing, 2021-01, Vol.30, p.4263-4274

2021

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Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 2022, 24 , 436

Entropy (Basel, Switzerland), 2022-05, Vol.24 (5), p.685

2022

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Medical Image Synthesis with Deep Convolutional Adversarial Networks

IEEE transactions on biomedical engineering, 2018-12, Vol.65 (12), p.2720-2730

2018

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Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation

Computer vision and image understanding, 2011-02, Vol.115 (2), p.256-269

2011

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A Tri-Attention fusion guided multi-modal segmentation network

Pattern recognition, 2022-04, Vol.124, p.108417, Article 108417

2022

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Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017, p.1177-1180

2017

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A literature survey of MR-based brain tumor segmentation with missing modalities

Computerized medical imaging and graphics, 2023-03, Vol.104 (102167), p.102167-102167, Article 102167

2023

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Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images

2017 IEEE International Conference on Image Processing (ICIP), 2017, p.4447-4451

2017

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Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities

Neurocomputing (Amsterdam), 2021-11, Vol.466, p.102-112

2021

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A review: Deep learning for medical image segmentation using multi-modality fusion

Automatic COVID‐19 CT segmentation using U‐Net integrated spatial and channel attention mechanism

Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions

Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention

Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier

Dissimilarity Metric Learning in the Belief Function Framework

Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review

Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction

Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions

Prediction of Lung Tumor Evolution During Radiotherapy in Individual Patients With PET

Predictive value of initial FDG-PET features for treatment response and survival in esophageal cancer patients treated with chemo-radiation therapy using a random forest classifier

Semi-automatic lymphoma detection and segmentation using fully conditional random fields

CUS-RF-Based Credit Card Fraud Detection with Imbalanced Data

Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction

Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation

A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction

Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities

Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 2022, 24 , 436

Medical Image Synthesis with Deep Convolutional Adversarial Networks

Kernel feature selection to fuse multi-spectral MRI images for brain tumor segmentation

A Tri-Attention fusion guided multi-modal segmentation network

Tumor delineation in FDG-PET images using a new evidential clustering algorithm with spatial regularization and adaptive distance metric

A literature survey of MR-based brain tumor segmentation with missing modalities

Accurate tumor segmentation in FDG-PET images with guidance of complementary CT images

Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities

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