Research output per year
Research output per year
Personal profile
Research interests
Exploring Medical Imaging potential in Radiotherapy using Deep Learning. Currently working on novel deep learning segmentation model which could be able to guide radiation oncologists in Head and Neck tumor segmentation on PET-CT images.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
Education/Academic qualification
Statistics, Masters Degree, MSc in Statistics and Machine Learning, Linkoping Univ, Linkoping University
Aug-2017 → Jun-2019
Award Date: 4-Jun-2019
Engineering, Bachelor Degree, BSc in Mathematics for Engineering, Politecnico di Torino
Oct-2013 → Oct-2016
Award Date: 26-Oct-2016
External positions
Research Scientist, ContextVision AB
Jan-2019 → Sept-2019
Data and Web Analyst, Drexcode
Oct-2016 → Jul-2017
Fingerprint
Dive into the research topics where Alessia de Biase is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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The prognostic value of pathologic lymph node imaging using deep learning-based outcome prediction in oropharyngeal cancer patients
Ma, B., De Biase, A., Guo, J., van Dijk, L. V., Langendijk, J. A., Both, S., van Ooijen, P. M. A. & Sijtsema, N. M., Jan-2025, In: Physics and imaging in radiation oncology. 33, 8 p., 100733.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile12 Downloads (Pure) -
Uncertainty-aware deep learning for segmentation of primary tumor and pathologic lymph nodes in oropharyngeal cancer: Insights from a multi-center cohort
De Biase, A., Sijtsema, N. M., van Dijk, L. V., Steenbakkers, R., Langendijk, J. A. & van Ooijen, P., Jul-2025, In: Computerized medical imaging and graphics. 123, 10 p., 102535.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile2 Downloads (Pure) -
Artificial intelligence in head and neck radiotherapy: towards personalized treatment
De Biase, A., 2024, [Groningen]: University of Groningen. 300 p.Research output: Thesis › Thesis fully internal (DIV)
Open AccessFile350 Downloads (Pure) -
Clinical adoption of deep learning target auto-segmentation for radiation therapy: challenges, clinical risks, and mitigation strategies
Biase, A. D., Sijtsema, N. M., Janssen, T., Hurkmans, C., Brouwer, C. & van Ooijen, P., Jan-2024, In: BJR|Artificial Intelligence. 1, 1, 14 p., ubae015.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile4 Downloads (Pure) -
Deep learning-based outcome prediction using PET/CT and automatically predicted probability maps of primary tumor in patients with oropharyngeal cancer
De Biase, A., Ma, B., Guo, J., van Dijk, L. V., Langendijk, J. A., Both, S., van Ooijen, P. M. A. & Sijtsema, N. M., Feb-2024, In: Computer Methods and Programs in Biomedicine. 244, 10 p., 107939.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile13 Citations (Scopus)109 Downloads (Pure)
Activities
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Empowering robust tumour segmentation in multi-centre OPC cohorts: DL tumour probability maps
de Biase, A. (Speaker)
4-May-2024Activity: Talk and presentation › Academic presentation › Academic
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Dive into Synthetic Data in Clinical Applications
de Biase, A. (Speaker)
28-Jun-2024Activity: Talk and presentation › Professional or public presentation › Professional
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Exploring Uncertainty-Aware Deep Learning for Segmentation of Primary Tumor and Pathologic Lymph Nodes in Oropharyngeal Cancer: Insights from a Multi-Center Cohort
de Biase, A. (Speaker)
27-Jun-2024Activity: Talk and presentation › Academic presentation › Academic
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EuSoMII Annual Meeting 2023
de Biase, A. (Attendee)
13-Oct-2023 → 14-Oct-2023Activity: Organising and attending an event › Attending an event › Academic
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Is one contour all we need? Rethinking the output of deep learning automatic tumor segmentation models for oropharyngeal cancer
de Biase, A. (Speaker)
15-May-2023Activity: Talk and presentation › Academic presentation › Academic