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2020 – today
- 2025
[j68]Ling Huang
, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and reliability learning for multimodal medical image segmentation. Inf. Fusion 113: 102648 (2025)- [j67]Zexin Ji, Beiji Zou, Xiaoyan Kui, Hua Li, Pierre Vera, Su Ruan:
Generation of super-resolution for medical image via a self-prior guided Mamba network with edge-aware constraint. Pattern Recognit. Lett. 187: 93-99 (2025) - 2024
- [j66]Fethi Ghazouani, Pierre Vera, Su Ruan:
Efficient brain tumor segmentation using Swin transformer and enhanced local self-attention. Int. J. Comput. Assist. Radiol. Surg. 19(2): 273-281 (2024) - [j65]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
Discriminative Hamiltonian variational autoencoder for accurate tumor segmentation in data-scarce regimes. Neurocomputing 606: 128360 (2024) - [j64]Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng:
A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Medical Image Anal. 97: 103223 (2024) - [c94]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Self-prior Guided Mamba-UNet Networks for Medical Image Super-Resolution. ICPR (11) 2024: 160-174 - [c93]Thibaud Brochet, Kangfu Han, Jiale Cheng, Fenqiang Zhao, Jérôme Lapuyade-Lahorgue, Su Ruan, Yi-Fang Tu, Sheng-Che Hung, Gang Li:
Neonatal Hypoxic Ischemic Encephalopathy Severity Grading Using Multimodal Swin Transformer. ISBI 2024: 1-4 - [c92]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes. ISBI 2024: 1-5 - [c91]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Deform-Mamba Network for MRI Super-Resolution. MICCAI (7) 2024: 242-252 - [i36]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
3D MRI Synthesis with Slice-Based Latent Diffusion Models: Improving Tumor Segmentation Tasks in Data-Scarce Regimes. CoRR abs/2406.05421 (2024) - [i35]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
Discriminative Hamiltonian Variational Autoencoder for Accurate Tumor Segmentation in Data-Scarce Regimes. CoRR abs/2406.11659 (2024) - [i34]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Deform-Mamba Network for MRI Super-Resolution. CoRR abs/2407.05969 (2024) - [i33]Zexin Ji, Beiji Zou, Xiaoyan Kui, Pierre Vera, Su Ruan:
Self-Prior Guided Mamba-UNet Networks for Medical Image Super-Resolution. CoRR abs/2407.05993 (2024) - [i32]Zacharia Mesbah, Léo Mottay, Romain Modzelewski, Pierre Decazes, Sébastien Hapdey, Su Ruan, Sébastien Thureau:
AutoPETIII: The Tracer Frontier. What Frontier? CoRR abs/2410.02807 (2024) - [i31]Ling Huang, Yucheng Xing, Qika Lin, Su Ruan, Mengling Feng:
EsurvFusion: An evidential multimodal survival fusion model based on Gaussian random fuzzy numbers. CoRR abs/2412.01215 (2024) - 2023
- [j63]Tongxue Zhou, Su Ruan, Haigen Hu:
A literature survey of MR-based brain tumor segmentation with missing modalities. Comput. Medical Imaging Graph. 104: 102167 (2023) - [j62]Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Fethi Ghazouani, Sébastien Thureau, Maxime Fontanilles, Su Ruan:
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning. Comput. Medical Imaging Graph. 106: 102218 (2023) - [j61]Ling Huang, Su Ruan, Thierry Denoeux:
Semi-supervised multiple evidence fusion for brain tumor segmentation. Neurocomputing 535: 40-52 (2023) - [j60]Ling Huang, Su Ruan, Thierry Denoeux:
Application of belief functions to medical image segmentation: A review. Inf. Fusion 91: 737-756 (2023) - [j59]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan:
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. J. Imaging 9(4): 81 (2023) - [c90]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Hua Li, Pierre Vera, Pierre Decazes, Su Ruan:
Prediction of Head-Neck Cancer Recurrence from Pet/CT Images with Havrda-Charvat Entropy. IPTA 2023: 1-5 - [c89]Lixin Zhang, Yulun Sun, Pierre Decazes, Su Ruan, Yu Guo, Hui Yu:
One-shot Learning for DLBCL Segmentation in Whole Body PET/CT Images. ISBI 2023: 1-4 - [c88]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
End-to-End Autoencoding Architecture for the Simultaneous Generation of Medical Images and Corresponding Segmentation Masks. MICAD 2023: 32-40 - [i30]Tongxue Zhou, Alexandra Noeuveglise, Romain Modzelewski, Fethi Ghazouani, Sébastien Thureau, Maxime Fontanilles, Su Ruan:
Prediction of brain tumor recurrence location based on multi-modal fusion and nonlinear correlation learning. CoRR abs/2304.13725 (2023) - [i29]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Su Ruan:
Deep Learning Approaches for Data Augmentation in Medical Imaging: A Review. CoRR abs/2307.13125 (2023) - [i28]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Deep evidential fusion with uncertainty quantification and contextual discounting for multimodal medical image segmentation. CoRR abs/2309.05919 (2023) - [i27]Ling Huang, Su Ruan, Yucheng Xing, Mengling Feng:
A review of uncertainty quantification in medical image analysis: probabilistic and non-probabilistic methods. CoRR abs/2310.06873 (2023) - [i26]Aghiles Kebaili, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
End-to-end autoencoding architecture for the simultaneous generation of medical images and corresponding segmentation masks. CoRR abs/2311.10472 (2023) - 2022
- [j58]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Multi-task multi-scale learning for outcome prediction in 3D PET images. Comput. Biol. Medicine 151(Part): 106208 (2022) - [j57]Zhengshan Huang, Yu Guo, Ning Zhang, Xian Huang, Pierre Decazes, Stéphanie Becker, Su Ruan:
Multi-scale feature similarity-based weakly supervised lymphoma segmentation in PET/CT images. Comput. Biol. Medicine 151(Part): 106230 (2022) - [j56]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 24(4): 436 (2022) - [j55]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Alexandre Huat, Sébastien Thureau, David Pasquier, Isabelle Gardin, Romain Modzelewski, David Gibon, Juliette Thariat, Vincent Grégoire, Pierre Vera, Su Ruan:
Correction: Brochet et al. A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction. Entropy 2022, 24, 436. Entropy 24(5): 685 (2022) - [j54]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. Int. J. Approx. Reason. 149: 39-60 (2022) - [j53]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction. J. Imaging 8(5): 130 (2022) - [j52]Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu:
A Tri-Attention fusion guided multi-modal segmentation network. Pattern Recognit. 124: 108417 (2022) - [j51]Haigen Hu, Leizhao Shen, Qiu Guan, Xiaoxin Li, Qianwei Zhou, Su Ruan:
Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images. Pattern Recognit. 124: 108452 (2022) - [j50]Tongxue Zhou, Pierre Vera, Stéphane Canu, Su Ruan:
Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation. Pattern Recognit. Lett. 158: 125-132 (2022) - [c87]Tongxue Zhou, Alexandra Noeuveglise, Fethi Ghazouani, Romain Modzelewski, Sébastien Thureau, Maxime Fontanilles, Su Ruan:
Prediction of Brain Tumor Recurrence Location Based on Kullback-Leibler Divergence and Nonlinear Correlation Learning. ICPR 2022: 4414-4419 - [c86]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Alexandre Huat, Sébastien Thureau, David Pasquier, Isabelle Gardin, Romain Modzelewski, David Gibon, Juliette Thariat, Vincent Grégoire, Pierre Vera, Su Ruan:
Deep Learning Based Radiomics to Predict Treatment Response Using Multi-datasets. MICAD 2022: 431-440 - [c85]Vincent Andrearczyk, Valentin Oreiller, Moamen Abobakr, Azadeh Akhavanallaf, Panagiotis Balermpas, Sarah Boughdad, Leo Capriotti, Joël Castelli, Catherine Cheze Le Rest, Pierre Decazes, Ricardo Correia, Dina El-Habashy, Hesham Elhalawani, Clifton D. Fuller, Mario Jreige, Yomna Khamis, Agustina La Greca Saint-Esteven, Abdallah Sherif Radwan Mohamed, Mohamed A. Naser, John O. Prior, Su Ruan, Stephanie Tanadini-Lang, Olena Tankyevych, Yazdan Salimi, Martin Vallières, Pierre Vera, Dimitris Visvikis, Kareem A. Wahid, Habib Zaidi, Mathieu Hatt, Adrien Depeursinge:
Overview of the HECKTOR Challenge at MICCAI 2022: Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT. HECKTOR@MICCAI 2022: 1-30 - [c84]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence Fusion with Contextual Discounting for Multi-modality Medical Image Segmentation. MICCAI (5) 2022: 401-411 - [i25]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Lymphoma segmentation from 3D PET-CT images using a deep evidential network. CoRR abs/2201.13078 (2022) - [i24]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Multi-Task Multi-Scale Learning For Outcome Prediction in 3D PET Images. CoRR abs/2203.00641 (2022) - [i23]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
A Quantitative Comparison between Shannon and Tsallis Havrda Charvat Entropies Applied to Cancer Outcome Prediction. CoRR abs/2203.11943 (2022) - [i22]Ling Huang, Su Ruan:
Application of belief functions to medical image segmentation: A review. CoRR abs/2205.01733 (2022) - [i21]Zong Fan, Xiaohui Zhang, Jacob A. Gasienica, Jennifer Potts, Su Ruan, Wade Thorstad, Hiram Gay, Xiaowei Wang, Hua Li:
A novel adversarial learning strategy for medical image classification. CoRR abs/2206.11501 (2022) - [i20]Ling Huang, Thierry Denoeux, Pierre Vera, Su Ruan:
Evidence fusion with contextual discounting for multi-modality medical image segmentation. CoRR abs/2206.11739 (2022) - 2021
- [j49]Shenghua He, Chunfeng Lian, Wade Thorstad, Hiram Gay, Yujie Zhao, Su Ruan, Xiaowei Wang, Hua Li:
A novel systematic approach for cancer treatment prognosis and its applications in oropharyngeal cancer with microRNA biomarkers. Bioinform. 37(19): 3106-3114 (2021) - [j48]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities. Neurocomputing 466: 102-112 (2021) - [j47]Tongxue Zhou, Stéphane Canu, Su Ruan:
Automatic COVID-19 CT segmentation using U-Net integrated spatial and channel attention mechanism. Int. J. Imaging Syst. Technol. 31(1): 16-27 (2021) - [j46]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Latent Correlation Representation Learning for Brain Tumor Segmentation With Missing MRI Modalities. IEEE Trans. Image Process. 30: 4263-4274 (2021) - [c83]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential Segmentation of 3D PET/CT Images. BELIEF 2021: 159-167 - [c82]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 Classification with Deep Neural Network and Belief Functions. BIBE 2021: 3:1-3:4 - [c81]Ling Huang, Su Ruan, Thierry Denoeux:
Belief Function-Based Semi-Supervised Learning For Brain Tumor Segmentation. ISBI 2021: 160-164 - [c80]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
A Dual Supervision Guided Attentional Network for Multimodal MR Brain Tumor Segmentation. MICAD 2021: 3-11 - [c79]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT Fusion with Dempster-Shafer Theory for Lymphoma Segmentation. MLMI@MICCAI 2021: 30-39 - [c78]Zong Fan, Shenghua He, Su Ruan, Xiaowei Wang, Hua Li:
Deep learning-based multi-class COVID-19 classification with x-ray images. Image-Guided Procedures 2021 - [i19]Ling Huang, Su Ruan, Thierry Denoeux:
Covid-19 classification with deep neural network and belief functions. CoRR abs/2101.06958 (2021) - [i18]Ling Huang, Su Ruan, Thierry Denoeux:
Belief function-based semi-supervised learning for brain tumor segmentation. CoRR abs/2102.00097 (2021) - [i17]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. CoRR abs/2102.03111 (2021) - [i16]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Sébastien Bougleux, Mathieu Salaün, Su Ruan:
Deep learning using Havrda-Charvat entropy for classification of pulmonary endomicroscopy. CoRR abs/2104.05450 (2021) - [i15]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Latent Correlation Representation Learning for Brain Tumor Segmentation with Missing MRI Modalities. CoRR abs/2104.06231 (2021) - [i14]Ling Huang, Su Ruan, Pierre Decazes, Thierry Denoeux:
Evidential segmentation of 3D PET/CT images. CoRR abs/2104.13293 (2021) - [i13]Ling Huang, Thierry Denoeux, David Tonnelet, Pierre Decazes, Su Ruan:
Deep PET/CT fusion with Dempster-Shafer theory for lymphoma segmentation. CoRR abs/2108.05422 (2021) - [i12]Fereshteh Yousefirizi, Pierre Decazes, Amine Amyar, Su Ruan, Babak Saboury, Arman Rahmim:
AI-Based Detection, Classification and Prediction/Prognosis in Medical Imaging: Towards Radiophenomics. CoRR abs/2110.10332 (2021) - [i11]Tongxue Zhou, Su Ruan, Pierre Vera, Stéphane Canu:
A Tri-attention Fusion Guided Multi-modal Segmentation Network. CoRR abs/2111.01623 (2021) - [i10]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Feature-enhanced Generation and Multi-modality Fusion based Deep Neural Network for Brain Tumor Segmentation with Missing MR Modalities. CoRR abs/2111.04735 (2021) - [i9]Haigen Hu, Leizhao Shen, Qiu Guan, Xiaoxin Li, Qianwei Zhou, Su Ruan:
Deep Co-supervision and Attention Fusion Strategy for Automatic COVID-19 Lung Infection Segmentation on CT Images. CoRR abs/2112.10368 (2021) - 2020
- [j45]Amine Amyar, Romain Modzelewski, Hua Li, Su Ruan:
Multi-task deep learning based CT imaging analysis for COVID-19 pneumonia: Classification and segmentation. Comput. Biol. Medicine 126: 104037 (2020) - [j44]Tongxue Zhou, Stéphane Canu, Su Ruan:
Fusion based on attention mechanism and context constraint for multi-modal brain tumor segmentation. Comput. Medical Imaging Graph. 86: 101811 (2020) - [j43]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian. Digit. Signal Process. 101: 102703 (2020) - [j42]Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen:
Corrections to "Medical Image Synthesis With Deep Convolutional Adversarial Networks". IEEE Trans. Biomed. Eng. 67(9): 2706 (2020) - [c77]Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski:
RADIOGAN: Deep Convolutional Conditional Generative Adversarial Network to Generate PET Images. ICBRA 2020: 28-33 - [c76]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
3D Medical Multi-modal Segmentation Network Guided by Multi-source Correlation Constraint. ICPR 2020: 10243-10250 - [c75]Thibaud Brochet, Jérôme Lapuyade-Lahorgue, Sébastien Bougleux, Mathieu Salaün, Su Ruan:
Deep learning based automatic detection of uninformative images in pulmonary optical endomicroscopy. IPTA 2020: 1-5 - [c74]Zoé Lambert, Caroline Petitjean, Bernard Dubray, Su Ruan:
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images. IPTA 2020: 1-6 - [c73]Yu Guo, Pierre Decazes, Stéphanie Becker, Hua Li, Su Ruan:
Deep Disentangled Representation Learning of Pet Images for Lymphoma Outcome Prediction. ISBI 2020: 1-4 - [c72]Tongxue Zhou, Su Ruan, Yu Guo, Stéphane Canu:
A Multi-Modality Fusion Network Based on Attention Mechanism for Brain Tumor Segmentation. ISBI 2020: 377-380 - [c71]Haigen Hu, Leizhao Shen, Tongxue Zhou, Pierre Decazes, Pierre Vera, Su Ruan:
Lymphoma Segmentation in PET Images Based on Multi-view and Conv3D Fusion Strategy. ISBI 2020: 1197-1200 - [c70]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Brain Tumor Segmentation with Missing Modalities via Latent Multi-source Correlation Representation. MICCAI (4) 2020: 533-541 - [i8]Amine Amyar, Romain Modzelewski, Pierre Vera, Vincent Morard, Su Ruan:
Weakly Supervised PET Tumor Detection Using Class Response. CoRR abs/2003.08337 (2020) - [i7]Amine Amyar, Su Ruan, Pierre Vera, Pierre Decazes, Romain Modzelewski:
RADIOGAN: Deep Convolutional Conditional Generative adversarial Network To Generate PET Images. CoRR abs/2003.08663 (2020) - [i6]Tongxue Zhou, Stéphane Canu, Pierre Vera, Su Ruan:
Brain tumor segmentation with missing modalities via latent multi-source correlation representation. CoRR abs/2003.08870 (2020) - [i5]Tongxue Zhou, Stéphane Canu, Su Ruan:
An automatic COVID-19 CT segmentation network using spatial and channel attention mechanism. CoRR abs/2004.06673 (2020) - [i4]Tongxue Zhou, Su Ruan, Stéphane Canu:
A review: Deep learning for medical image segmentation using multi-modality fusion. CoRR abs/2004.10664 (2020)
2010 – 2019
- 2019
- [j41]Tongxue Zhou, Su Ruan, Stéphane Canu:
A review: Deep learning for medical image segmentation using multi-modality fusion. Array 3-4: 100004 (2019) - [j40]Haigen Hu, Pierre Decazes, Pierre Vera, Hua Li, Su Ruan:
Detection and segmentation of lymphomas in 3D PET images via clustering with entropy-based optimization strategy. Int. J. Comput. Assist. Radiol. Surg. 14(10): 1715-1724 (2019) - [j39]Fan Wang, Chunfeng Lian, Pierre Vera, Su Ruan:
Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG-PET images. Multim. Syst. 25(2): 127-133 (2019) - [j38]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
Mixed Integer Programming For Sparse Coding: Application to Image Denoising. IEEE Trans. Computational Imaging 5(3): 354-365 (2019) - [j37]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. IEEE Trans. Image Process. 28(2): 755-766 (2019) - [c69]Haigen Hu, Chao Du, Qiu Guan, Qianwei Zhou, Pierre Vera, Su Ruan:
A Background-based Data Enhancement Method for Lymphoma Segmentation in 3D PET Images. BIBM 2019: 1194-1196 - [c68]Haigen Hu, Chao Du, Pierre Decazes, Pierre Vera, Su Ruan:
A Prior Knowledge Intergrated Scheme for Detection and Segmentation of Lymphomas in 3D PET Images based on DBSCAN and GAs. BIBM 2019: 2413-2420 - [c67]Haigen Hu, Pierre Decazes, Jérôme Lapuyade-Lahorgue, Pierre Vera, Su Ruan:
Gaussian-based Spatial Hybrid Distances for Detection and Segmentation of Lymphoid Lesions in 3D PET Images. CISP-BMEI 2019: 1-5 - [c66]Tongxue Zhou, Su Ruan, Haigen Hu, Stéphane Canu:
Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI. MLMI@MICCAI 2019: 574-582 - [e1]Caroline Petitjean, Su Ruan, Zoé Lambert, Bernard Dubray:
Proceedings of the 2019 Challenge on Segmentation of THoracic Organs at Risk in CT Images, SegTHOR@ISBI 2019, April 8, 2019. CEUR Workshop Proceedings 2349, CEUR-WS.org 2019 [contents] - [i3]Zoé Lambert, Caroline Petitjean, Bernard Dubray, Su Ruan:
SegTHOR: Segmentation of Thoracic Organs at Risk in CT images. CoRR abs/1912.05950 (2019) - 2018
- [j36]Yuntao Yu, Pierre Decazes, Jérôme Lapuyade-Lahorgue, Isabelle Gardin, Pierre Vera, Su Ruan:
Semi-automatic lymphoma detection and segmentation using fully conditional random fields. Comput. Medical Imaging Graph. 70: 1-7 (2018) - [j35]Jian Wu, Thomas R. Mazur, Su Ruan, Chunfeng Lian, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Mark A. Anastasio, H. Michael Gach, Sasa Mutic, Maria Thomas, Hua Li:
A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. Medical Image Anal. 47: 68-80 (2018) - [j34]Chunfeng Lian, Su Ruan, Thierry Denoeux, Hua Li, Pierre Vera:
Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. IEEE Trans. Biomed. Eng. 65(1): 21-30 (2018) - [j33]Dong Nie, Roger Trullo, Jun Lian, Li Wang, Caroline Petitjean, Su Ruan, Qian Wang, Dinggang Shen:
Medical Image Synthesis with Deep Convolutional Adversarial Networks. IEEE Trans. Biomed. Eng. 65(12): 2720-2730 (2018) - [c65]Naouel Boughattas, Maxime Berar, Kamel Hamrouni, Su Ruan:
Feature selection and classification using multiple kernel learning for brain tumor segmentation. ATSIP 2018: 1-5 - [c64]Jierui Zha, Pierre Decazes, Jérôme Lapuyade, Abderrahim Elmoataz, Su Ruan:
3D lymphoma detection in PET-CT images with supervoxel and CRFs. IPTA 2018: 1-5 - [c63]Chunfeng Lian, Hua Li, Pierre Vera, Su Ruan:
Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion. ISBI 2018: 220-223 - [c62]Jian Wu, Su Ruan, Chunfeng Lian, Sasa Mutic, Mark A. Anastasio, Hua Li:
Active learning with noise modeling for medical image annotation. ISBI 2018: 298-301 - [c61]Jian Wu, Su Ruan, Thomas R. Mazur, Nalini Daniel, Hilary Lashmett, Laura Ochoa, Imran Zoberi, Chunfeng Lian, H. Michael Gach, Sasa Mutic, Maria Thomas, Mark A. Anastasio, Hua Li:
Heart motion tracking on cine MRI based on a deep Boltzmann machine-driven level set method. ISBI 2018: 1153-1156 - [c60]Yuan Liu, Stéphane Canu, Paul Honeine, Su Ruan:
K-SVD with a Real ℓ0 Optimization: Application to Image Denoising. MLSP 2018: 1-6 - 2017
- [j32]Paul Desbordes, Su Ruan, Romain Modzelewski, Vauclin Sébastien, Pierre Vera, Isabelle Gardin:
Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. Comput. Medical Imaging Graph. 60: 42-49 (2017) - [j31]Jérôme Lapuyade-Lahorgue, Jing-Hao Xue, Su Ruan:
Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions. IEEE Trans. Image Process. 26(7): 3187-3195 (2017) - [c59]