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Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics

Scientific reports, 2020-07, Vol.10 (1), p.12340-12340, Article 12340

2020

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The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features

Scientific reports, 2019-03, Vol.9 (1), p.4329, Article 4329

2019

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Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives

Methods (San Diego, Calif.), 2021-04, Vol.188, p.44-60

2021

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AutoComBat: a generic method for harmonizing MRI-based radiomic features

Scientific reports, 2022-07, Vol.12 (1), p.12762-12762, Article 12762

2022

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Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis

Frontiers in oncology, 2021-07, Vol.11, p.638262-638262

2021

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Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Frontiers in computational neuroscience, 2020-03, Vol.14, p.17-17

2020

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Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Journal for immunotherapy of cancer, 2022-07, Vol.10 (7), p.e004848

2022

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Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

International journal of radiation oncology, biology, physics, 2020-11, Vol.108 (3), p.813-823

2020

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Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, p.253-266

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Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, p.327-339

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Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

Journal for immunotherapy of cancer, 2022-10, Vol.10 (10), p.e004867

2022

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A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers

European journal of nuclear medicine and molecular imaging, 2023-11, Vol.50 (13), p.4010-4023

2023

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COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

IEEE/ACM transactions on computational biology and bioinformatics, 2022-11, Vol.19 (6), p.3317-3331

2022

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Commentary: Toward an Improved Understanding of Recidivism

The journal of the American Academy of Psychiatry and the Law, 2012-01, Vol.40 (2), p.232-233

Alexandre J. R. Carré

2012

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Reinventing Radiation Therapy with Machine Learning and Imaging Bio-markers (Radiomics): state-ofthe-art, challenges and perspectives Authors

Methods (San Diego, Calif.), 2021-04, Vol.188, p.44-60

2021

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Deep-Learning for Rapid Estimation of the Out-of-Field Dose in External Beam Photon Radiation Therapy – A Proof of Concept

International journal of radiation oncology, biology, physics, 2024-03

2024

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Prediction of brain metastases progression and overall survival in patients with metastatic non-small cell lung cancer treated by immune checkpoint inhibitors using a radiomic model

Journal of clinical oncology, 2023-06, Vol.41 (16_suppl), p.e21169-e21169

2023

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Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation

Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, p.470-479

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Correction: Ipilimumab plus nivolumab for patients with metastatic uveal melanoma: a multicenter, retrospective study

Journal for immunotherapy of cancer, 2020-11, Vol.8 (2), p.e000331corr1

2020

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Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data, p.87-93

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Elucidation of roles for vitamin B 12 in regulation of folate, ubiquinone, and methionine metabolism

Proceedings of the National Academy of Sciences - PNAS, 2017-02, Vol.114 (7), p.E1205

2017

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Elucidation of roles for vitamin B12 in regulation of folate, ubiquinone, and methionine metabolism

Proceedings of the National Academy of Sciences - PNAS, 2017-02, Vol.114 (7), p.E1205-E1214

2017

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U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets

Medical Image Computing and Computer Assisted Intervention – MICCAI 2019, p.310-319

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Elucidation of roles for vitamin B^sub 12^ in regulation of folate, ubiquinone, and methionine metabolism

Proceedings of the National Academy of Sciences - PNAS, 2017-02, Vol.114 (7), p.E1205

2017

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Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics

The complexity of tumor shape, spiculatedness, correlates with tumor radiomic shape features

Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives

AutoComBat: a generic method for harmonizing MRI-based radiomic features

Development of a Machine Learning Classifier Based on Radiomic Features Extracted From Post-Contrast 3D T1-Weighted MR Images to Distinguish Glioblastoma From Solitary Brain Metastasis

Deep Learning-Based Concurrent Brain Registration and Tumor Segmentation

Imaging approaches and radiomics: toward a new era of ultraprecision radioimmunotherapy?

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

Automatic Brain Tumor Segmentation with a Bridge-Unet Deeply Supervised Enhanced with Downsampling Pooling Combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm

Radiomics to predict outcomes and abscopal response of patients with cancer treated with immunotherapy combined with radiotherapy using a validated signature of CD8 cells

Brain Tumor Segmentation with Self-ensembled, Deeply-Supervised 3D U-Net Neural Networks: A BraTS 2020 Challenge Solution

Radiomics to evaluate interlesion heterogeneity and to predict lesion response and patient outcomes using a validated signature of CD8 cells in advanced melanoma patients treated with anti-PD1 immunotherapy

A common [18F]-FDG PET radiomic signature to predict survival in patients with HPV-induced cancers

COMBING: Clustering in Oncology for Mathematical and Biological Identification of Novel Gene Signatures

Commentary: Toward an Improved Understanding of Recidivism

Reinventing Radiation Therapy with Machine Learning and Imaging Bio-markers (Radiomics): state-ofthe-art, challenges and perspectives Authors

Deep-Learning for Rapid Estimation of the Out-of-Field Dose in External Beam Photon Radiation Therapy – A Proof of Concept

Prediction of brain metastases progression and overall survival in patients with metastatic non-small cell lung cancer treated by immune checkpoint inhibitors using a radiomic model

Weakly Supervised Multiple Instance Learning Histopathological Tumor Segmentation

Correction: Ipilimumab plus nivolumab for patients with metastatic uveal melanoma: a multicenter, retrospective study

Deep Learning Based Registration Using Spatial Gradients and Noisy Segmentation Labels

Elucidation of roles for vitamin B 12 in regulation of folate, ubiquinone, and methionine metabolism

Elucidation of roles for vitamin B12 in regulation of folate, ubiquinone, and methionine metabolism

U-ReSNet: Ultimate Coupling of Registration and Segmentation with Deep Nets

Elucidation of roles for vitamin B^sub 12^ in regulation of folate, ubiquinone, and methionine metabolism

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