| Deep co-supervision and attention fusion strategy for automatic COVID-19 lung infection segmentation on CT images | 1 | 0.34 | 2022 |
| Missing Data Imputation via Conditional Generator and Correlation Learning for Multimodal Brain Tumor Segmentation | 0 | 0.34 | 2022 |
| A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction (vol 24, 436, 2022) | 0 | 0.34 | 2022 |
| A Quantitative Comparison between Shannon and Tsallis-Havrda-Charvat Entropies Applied to Cancer Outcome Prediction | 0 | 0.34 | 2022 |
| Weakly Supervised Tumor Detection in PET Using Class Response for Treatment Outcome Prediction | 0 | 0.34 | 2022 |
| A Tri-Attention fusion guided multi-modal segmentation network | 0 | 0.34 | 2022 |
| Feature-enhanced generation and multi-modality fusion based deep neural network for brain tumor segmentation with missing MR modalities | 0 | 0.34 | 2021 |
| Automatic Covid-19 Ct Segmentation Using U-Net Integrated Spatial And Channel Attention Mechanism | 7 | 0.53 | 2021 |
| Fusion Based On Attention Mechanism And Context Constraint For Multi-Modal Brain Tumor Segmentation | 0 | 0.34 | 2020 |
| Multi-Task Deep Learning Based Ct Imaging Analysis For Covid-19 Pneumonia: Classification And Segmentation | 17 | 1.04 | 2020 |
| Incoherent dictionary learning via mixed-integer programming and hybrid augmented Lagrangian | 0 | 0.34 | 2020 |
| SegTHOR: Segmentation of Thoracic Organs at Risk in CT images | 0 | 0.34 | 2020 |
| Corrections to “Medical Image Synthesis With Deep Convolutional Adversarial Networks” [Mar 18 2720-2730] | 0 | 0.34 | 2020 |
| Mixed Integer Programming for Sparse Coding: Application to Image Denoising | 0 | 0.34 | 2019 |
| Joint Tumor Segmentation in PET-CT Images Using Co-Clustering and Fusion Based on Belief Functions. | 5 | 0.41 | 2019 |
| Deep Learning Model Integrating Dilated Convolution and Deep Supervision for Brain Tumor Segmentation in Multi-parametric MRI. | 0 | 0.34 | 2019 |
| Adaptive kernelized evidential clustering for automatic 3D tumor segmentation in FDG–PET images | 0 | 0.34 | 2019 |
| Semi-automatic lymphoma detection and segmentation using fully conditional random fields. | 0 | 0.34 | 2018 |
| Spatial Evidential Clustering With Adaptive Distance Metric for Tumor Segmentation in FDG-PET Images. | 2 | 0.39 | 2018 |
| Active learning with noise modeling for medical image annotation | 0 | 0.34 | 2018 |
| Unsupervised co-segmentation of tumor in PET-CT images using belief functions based fusion | 0 | 0.34 | 2018 |
| Feature selection and classification using multiple kernel learning for brain tumor segmentation | 0 | 0.34 | 2018 |
| Heart Motion Tracking On Cine Mri Based On A Deep Boltzmann Machine-Driven Level Set Method | 0 | 0.34 | 2018 |
| Medical Image Synthesis with Deep Convolutional Adversarial Networks. | 18 | 0.78 | 2018 |
| A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images. | 1 | 0.37 | 2018 |
| 3D Lymphoma Segmentation in PET/CT Images Based on Fully Connected CRFs. | 0 | 0.34 | 2017 |
| Feature selection for outcome prediction in oesophageal cancer using genetic algorithm and random forest classifier. | 2 | 0.36 | 2017 |
| Segmenting Multi-Source Images Using Hidden Markov Fields With Copula-Based Multivariate Statistical Distributions. | 2 | 0.36 | 2017 |
| Joint Segmentation Of Multiple Thoracic Organs In Ct Images With Two Collaborative Deep Architectures | 4 | 0.41 | 2017 |
| Comparison of 2D and 3D region-based deformable models and random walker methods for PET segmentation | 0 | 0.34 | 2016 |
| Dissimilarity Metric Learning in the Belief Function Framework. | 6 | 0.43 | 2016 |
| Joint Feature Transformation and Selection Based on Dempster-Shafer Theory. | 3 | 0.39 | 2016 |
| Multilabel statistical shape prior for image segmentation. | 3 | 0.40 | 2016 |
| Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. | 10 | 0.50 | 2016 |
| Segmentation Of Pelvic Organs At Risk Using Superpixels And Graph Diffusion In Prostate Radiotherapy | 0 | 0.34 | 2015 |
| An evidential classifier based on feature selection and two-step classification strategy | 20 | 0.74 | 2015 |
| Outcome prediction in tumour therapy based on Dempster-Shafer theory | 1 | 0.36 | 2015 |
| Right ventricle segmentation from cardiac MRI: a collation study. | 38 | 1.54 | 2015 |
| Dempster-Shafer Theory Based Feature Selection with Sparse Constraint for Outcome Prediction in Cancer Therapy. | 3 | 0.40 | 2015 |
| Joint tumor growth prediction and tumor segmentation on therapeutic follow-up PET images. | 2 | 0.37 | 2015 |
| Robust feature selection to predict tumor treatment outcome | 6 | 0.47 | 2015 |
| Statistical models of shape and spatial relation-application to hippocampus segmentation | 3 | 0.42 | 2014 |
| Segmentation of heterogeneous or small FDG PET positive tissue based on a 3D-locally adaptive random walk algorithm. | 10 | 0.71 | 2014 |
| Brain tumor segmentation from multiple MRI sequences using multiple kernel learning | 0 | 0.34 | 2014 |
| Dealing with uncertainty and imprecision in image segmentation using belief function theory. | 4 | 0.43 | 2014 |
| Prostate cancer segmentation from multiparametric MRI based on fuzzy Bayesian model | 0 | 0.34 | 2014 |
| Automatic lung tumor segmentation on PET images based on random walks and tumor growth model | 0 | 0.34 | 2014 |
| Advanced approach for PET breast cancer segmentation based on FAMIS methodology | 0 | 0.34 | 2014 |
| Fusion of multi-tracer PET images for dose painting. | 5 | 0.42 | 2014 |
| Eikonal-based region growing for efficient clustering. | 4 | 0.44 | 2014 |
