- Machine Learning and Oversampling techniques in prediction of urinary toxicity after radiotherapy for prostate cancer
- Predicting rectal bleeding in prostate cancer from dose volume histogram in a multicentric context
- Voxel-Based Analysis for Identification of Urethrovesical Subregions Predicting Urinary Toxicity After Prostate Cancer Radiation Therapy
- Local dose analysis to predict acute and late urinary toxicities after prostate cancer radiotherapy : assessment of cohort and method effects.
- Comparison of machine learning algorithms and oversampling techniques for urinary toxicity prediction after prostate cancer radiotherapy.
- Bladder and urethra subregions predicting urinary toxicity after prostate cancer radiotherapy.
- Predicting urinary toxicity via 2D and 3D dose map analyses in prostate cancer radiotherapy
- Relying on deep convolutional neural networks on PET/CT images for stage II and III non-small cell lung cancer outcome prediction
- Identification de sous-régions rectale et urétrovésicales hautement prédictives de toxicité en cas d’irradiation prostatique.
- Analysis of the urethro-vesical region for urinary toxicity prediction after prostate radiotherapy.
- A 3D Deep Convolutional Neural Network for Lung Cancer Survival Prediction Using Transfer Learning
- Multi-atlas-based segmentation of prostatic urethra from planning CT imaging to quantify dose distribution in prostate cancer radiotherapy
- Développement d’une méthode de segmentation automatique de l’urètre sur tomodensitométrie de planification permettant d’évaluer la dose urétrale en cas de radiothérapie prostatique.
