Lisanne van Dijk
Orcid: 0000-0002-9515-5616
According to our database1,
Lisanne van Dijk
authored at least 16 papers
between 2020 and 2024.
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Bibliography
2024
Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer.
Comput. Methods Programs Biomed., 2024
Probability maps for deep learning-based head and neck tumor segmentation: Graphical User Interface design and test.
Comput. Biol. Medicine, 2024
2023
Comput. Graph. Forum, June, 2023
TransRP: Transformer-based PET/CT feature extraction incorporating clinical data for recurrence-free survival prediction in oropharyngeal cancer.
Proceedings of the Medical Imaging with Deep Learning, 2023
Improving Prediction of Late Symptoms using LSTM and Patient-reported Outcomes for Head and Neck Cancer Patients.
Proceedings of the 11th IEEE International Conference on Healthcare Informatics, 2023
2022
IEEE Trans. Vis. Comput. Graph., 2022
Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images.
CoRR, 2022
Deep Learning and Radiomics Based PET/CT Image Feature Extraction from Auto Segmented Tumor Volumes for Recurrence-Free Survival Prediction in Oropharyngeal Cancer Patients.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022
Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2022
2021
Combining Tumor Segmentation Masks with PET/CT Images and Clinical Data in a Deep Learning Framework for Improved Prognostic Prediction in Head and Neck Squamous Cell Carcinoma.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2021
Progression Free Survival Prediction for Head and Neck Cancer Using Deep Learning Based on Clinical and PET/CT Imaging Data.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2021
Head and Neck Cancer Primary Tumor Auto Segmentation Using Model Ensembling of Deep Learning in PET/CT Images.
Proceedings of the Head and Neck Tumor Segmentation and Outcome Prediction, 2021
Predicting late symptoms of head and neck cancer treatment using LSTM and patient reported outcomes.
Proceedings of the IDEAS 2021: 25th International Database Engineering & Applications Symposium, 2021
Proceedings of the Artificial Intelligence in Medicine, 2021
2020
Proceedings of the 31st IEEE Visualization Conference, 2020
Tumor Segmentation in Patients with Head and Neck Cancers Using Deep Learning Based-on Multi-modality PET/CT Images.
Proceedings of the Head and Neck Tumor Segmentation - First Challenge, 2020