| 18F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks L Sibille, R Seifert, N Avramovic, T Vehren, B Spottiswoode, S Zuehlsdorff, ... Radiology 294 (2), 445-452, 2020 | 158 | 2020 |
| Deep-learning 18F-FDG uptake classification enables total metabolic tumor volume estimation in diffuse large B-cell lymphoma N Capobianco, M Meignan, AS Cottereau, L Vercellino, L Sibille, ... Journal of Nuclear medicine 62 (1), 30-36, 2021 | 86 | 2021 |
| Whole-body uptake classification and prostate cancer staging in 68Ga-PSMA-11 PET/CT using dual-tracer learning N Capobianco, L Sibille, M Chantadisai, A Gafita, T Langbein, G Platsch, ... European journal of nuclear medicine and molecular imaging, 1-10, 2022 | 37 | 2022 |
| The autopet challenge: towards fully automated lesion segmentation in oncologic pet/ct imaging S Gatidis, M Früh, M Fabritius, S Gu, K Nikolaou, C La Fougère, J Ye, J He, ... | 33 | 2023 |
| Evaluation of an automatic classification algorithm using convolutional neural networks in oncological positron emission tomography P Pinochet, F Eude, S Becker, V Shah, L Sibille, MN Toledano, ... Frontiers in Medicine 8, 628179, 2021 | 28 | 2021 |
| PET uptake classification in lymphoma and lung cancer using deep learning L Sibille, N Avramovic, B Spottiswoode, M Schaefers, S Zuehlsdorff, ... Journal of Nuclear Medicine 59 (supplement 1), 325-325, 2018 | 9 | 2018 |
| Transfer learning of AI-based uptake classification from 18F-FDG PET/CT to 68Ga-PSMA-11 PET/CT for whole-body tumor burden assessment N Capobianco, A Gafita, G Platsch, L Sibille, B Spottiswoode, M Eiber, ... Journal of Nuclear Medicine 61 (supplement 1), 1411-1411, 2020 | 7 | 2020 |
| Whole-body tumor segmentation of 18f-fdg pet/ct using a cascaded and ensembled convolutional neural networks L Sibille, X Zhan, L Xiang arXiv preprint arXiv:2210.08068, 2022 | 4 | 2022 |
| An AI system to determine reconstruction parameters and improve PET image quality F Gao, V Shah, L Sibille, S Zuehlsdorff Journal of Nuclear Medicine 59 (supplement 1), 31-31, 2018 | 4 | 2018 |
| Comparison of Two Methods, with and without MRI, for Quantification of Florbetaben (F-18) PET C Hutton, L Sibille, S Bullich, A Catafau, N Koglin, R Pfeiffer, J Declerck EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 41, S324-S324, 2014 | 3 | 2014 |
| Multitask learning-to-rank neural network for predicting survival of diffuse large B-cell lymphoma patients from their unsegmented baseline [18F] FDG-PET/CT scans. L Rebaud, N Capobianco, L Sibille, K Girum, M Meignan, AS Cottereau, ... Journal of Nuclear Medicine 63 (supplement 2), 3250-3250, 2022 | 2 | 2022 |
| Inter-frame motion correction in whole-body direct parametric image reconstruction J Hu, L Sibille, B Spottiswoode US Patent 11,222,447, 2022 | 2 | 2022 |
| Methods and apparatus for artificial intelligence informed radiological reporting and model refinement BS Spottiswoode, L Sibille, V Simcic US Patent 11,386,991, 2022 | 1 | 2022 |
| Evaluation of the prognostic value of tumor fragmentation on [18F]-FDG PET/CT on an independent cohort of diffuse large B-cell lymphoma patients L Rebaud, N Capobianco, L Sibille, K Girum, M Meignan, AS Cottereau, ... Journal of Nuclear Medicine 63 (supplement 2), 3172-3172, 2022 | 1 | 2022 |
| Neural network for neurodegenerative disease classification R Fahmi, S Zuehlsdorff, L Sibille US Patent 10,769,785, 2020 | 1 | 2020 |
| Whole-body lesion detection and prostate cancer staging in Ga-68-PSMA-11 PET/CT using deep learning N Capobianco, A Gafita, G Platsch, L Sibille, B Spottiswoode, M Eiber, ... EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 47 (SUPPL 1 …, 2020 | 1 | 2020 |
| Fully automated deep learning FDG uptake classification enables Total Metabolic Tumor Volume (MTV) estimation in diffuse large B-cell lymphoma with similar predictive value as … N Capobianco, M Meignan, AS Cottereau, L Vercellino, L Sibille, ... Journal of Nuclear Medicine 61 (supplement 1), 504-504, 2020 | 1 | 2020 |
| System and method for retrieval of similar findings from a hybrid image dataset MD Kelly, D Schottlander, L Sibille US Patent 10,176,612, 2019 | 1 | 2019 |
| Visual Explanation of Classification L Sibille US Patent App. 18/246,338, 2023 | | 2023 |
| Multi‐branch convolutional neural network for Alzheimer’s Disease versus normal control classification using PET images R Sharma, L Sibille, R Fahmi Alzheimer's & Dementia 19, e061092, 2023 | | 2023 |
