| Skip-SCSE multi-scale attention and co-learning method for oropharyngeal tumor segmentation on multi-modal PET-CT images A De Biase, W Tang, N Sourlos, B Ma, J Guo, NM Sijtsema, P van Ooijen 3D Head and Neck Tumor Segmentation in PET/CT Challenge, 109-120, 2021 | 11 | 2021 |
| Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images A De Biase, NM Sijtsema, LV van Dijk, JA Langendijk, PMA van Ooijen Physics in Medicine & Biology 68 (5), 055013, 2023 | 10 | 2023 |
| Self-supervised multi-modality image feature extraction for the progression free survival prediction in head and neck cancer B Ma, J Guo, A De Biase, N Sourlos, W Tang, P van Ooijen, S Both, ... 3D Head and Neck Tumor Segmentation in PET/CT Challenge, 308-317, 2021 | 9 | 2021 |
| Standardization of artificial intelligence development in radiotherapy A de Biase, N Sourlos, PMA van Ooijen Seminars in radiation oncology 32 (4), 415-420, 2022 | 5 | 2022 |
| Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer A De Biase, B Ma, J Guo, LV van Dijk, JA Langendijk, S Both, ... Computer Methods and Programs in Biomedicine 244, 107939, 2024 | 2 | 2024 |
| Generative Adversarial Networks to enhance decision support in digital pathology A De Biase | 2 | 2019 |
| Swin UNETR for Tumor and Lymph Node Segmentation Using 3D PET/CT Imaging: A Transfer Learning Approach H Chu, LR De la O Arévalo, W Tang, B Ma, Y Li, A De Biase, S Both, ... 3D Head and Neck Tumor Segmentation in PET/CT Challenge, 114-120, 2022 | 1 | 2022 |
| Slice-by-slice deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for spatial uncertainty on FDG PET and CT images A De Biase, NM Sijtsema, L van Dijk, JA Langendijk, P van Ooijen arXiv preprint arXiv:2207.01623, 2022 | 1 | 2022 |
| PO-1606 Slice-by-slice deep learning aided oropharyngeal cancer segmentation on PET and CT images A De Biase, NM Sijtsema, JA Langendijk, LV van Dijk, PM van Ooijen Radiotherapy and Oncology 170, S1392-S1394, 2022 | 1 | 2022 |
| Fetal echogenic bowel: what is real echogenicity? A quantitative method based on histogram analysis of the grayscale S Spinnato, A De Biase, CM Bilardo, A Elvan-Taşpınar Fetal Diagnosis and Therapy 51 (2), 145-153, 2024 | | 2024 |
| Uncertainty-Aware Deep Learning for Segmentation of Primary Tumour and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Centre Cohort A De Biase, NM Sijtsema, LV van Dijk, R Steenbakkers, JA Langendijk, ... | | 2024 |
| PO-1656 autoencoder-based quality assurance of deep learning segmentation of parotid glands in HNC patients SW Zijlstra, A de Biase, C Brouwer, S Both, J Langendijk, P van Ooijen Radiotherapy and Oncology 182, S1357-S1358, 2023 | | 2023 |
| PD-0167 Predicted tumour probability maps improve deep learning outcome prediction in oropharyngeal cancer B Ma, A De Biase, J Guo, LV van Dijk, JA Langendijk, S Both, ... Radiotherapy and Oncology 182, S127-S128, 2023 | | 2023 |
| MO-0800 Is one contour all we need? Rethinking the output of DL tumour auto-segmentation models for OPC A De Biase, NM Sijtsema, L van Dijk, R Steenbakkers, J Langendijk, ... Radiotherapy and Oncology 182, S671-S672, 2023 | | 2023 |
| PO-1777 Self-supervised image feature extraction for outcomes prediction in oropharyngeal cancer B Ma, J Guo, H Chu, A De Biase, N Sourlos, W Tang, JA Langendijk, ... Radiotherapy and Oncology 170, S1583-S1584, 2022 | | 2022 |
| Deep Learning Data Augmentation Approach to Improve Cancer Segmentation Performance across Different Scanners A De Biase, N Burlutskiy, N Pinchaud, A Eklund Nordic Symposium on Digital Pathology, 2019 | | 2019 |
Peter van OoijenUniversity of Groningen, Center for Medical Imaging North East NetherlandsVerified email at umcg.nl
