Inaugural Edition: Monthly DIAG News - March
New DIAG members
We are thrilled to welcome the following new members to DIAG:
- Carlijn Lems
- Hartmut Häntze
- Joske van der Zande
- Judith Grolleman
- Karlijn Bok
- Roland Nemeth
- Sandrine Nugteren
Awards and grants
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Jeroen van der Laak, Geert Litjens, and Francesco Ciompi were awarded the 2024 Ammodo Science Award for groundbreaking research. Read more about this achievement in this news item.
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Francesco Ciompi, alongside Marloes Groot and Jan Willem Duitman, has also secured a grant through the Open Technology Programme from the NWO domain of Applied and Technical Sciences. This funding supports their collaborative research project "DoPredict: Dynamic 3D Biopsy Based Response to Treatment Prediction", led by Prof. Marloes Groot. The project aims to develop a system for predicting treatment outcomes in lung diseases by testing various medications on small tissue samples from patients.
Upcoming events
Stay tuned for upcoming events where our team will be presenting our research findings and engaging with the broader medical imaging community. Here are some events to mark on your calendar:
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The inaugural lecture by Professor Geert Litjens: Geert Litjens will deliver his inaugural lecture on Thursday, April 4th, at 3:45 PM. He will speak about AI for medical image analysis in pathology and radiology during his lecture, which is titled: "Van tweede viool naar eenmansband: de toekomst van medische AI”.
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DIAG Day: The 5-weekly DIAG Day for all DIAG members will take place on April 10th (Update: this DIAG day is cancelled).
Stay tuned for more exciting updates in the months ahead!
Highlighted publications
We are excited to share some of our recent publications!
← Back to overview"Automated Mitotic Spindle Hotspot Counts and Clinical Outcomes in Early-Stage Triple-Negative Breast Cancer" - R. Leon-Ferre, J. Carter, D. Zahrieh, J. Sinnwell, R. Salgado, V. Suman, D. Hillman, J. Boughey, K. Kalari, F. Couch, J. Ingle, M. Balkenhol, F. Ciompi, J. van der Laak, M. Goetz; npj Breast Cancer, 2024;10.
"Enhancing Deep Learning Model for Pulmonary Nodule Malignancy Risk Estimation in Chest CT with Uncertainty Estimation" - D. Peeters, N. Alves, K. Venkadesh, R. Dinnessen, Z. Saghir, E. Scholten, C. Schaefer-Prokop, R. Vliegenthart, M. Prokop, C. Jacobs; European Radiology, 2024.
"Combining Public Datasets for Automated Tooth Assessment in Panoramic Radiographs"- N. van Nistelrooij, K. Ghoul, T. Xi, A. Saha, S. Kempers, M. Cenci, B. Loomans, T. Flügge, B. van Ginneken, S. Vinayahalingam; BMC Oral Health, 2024;24.
"Comparing Deep Learning and Pathologist Quantification of PD-L1 Expression in Non-Small Cell Lung Cancer Whole-Slide Images" - L. van Eekelen, J. Spronck, M. Looijen-Salamon, S. Vos, E. Munari, I. Girolami, A. Eccher, B. Acs, C. Boyaci, G. de Souza, M. Demirel-Andishmand, L. Meesters, D. Zegers, L. van der Woude, W. Theelen, M. van den Heuvel, K. Grünberg, B. van Ginneken, J. van der Laak, F. Ciompi; Scientific Reports, 2024;14.
"Artificial Intelligence in Medicine: Mitigating Risks and Maximizing Benefits via Quality Assurance, Quality Control, and Acceptance Testing" - U. Mahmood, A. Shukla-Dave, H. Chan, K. Drukker, R. Samala, Q. Chen, D. Vergara, H. Greenspan, N. Petrick, B. Sahiner, Z. Huo, R. Summers, K. Cha, G. Tourassi, T. Deserno, K. Grizzard, J. Näppi, H. Yoshida, D. Regge, R. Mazurchuk, K. Suzuki, L. Morra, H. Huisman, S. Armato, L. Hadjiiski; BJR|Artificial Intelligence, 2024;1.
"Lumbar Spine Segmentation in MR Images: A Dataset and a Public Benchmark" - J. van der Graaf, M. van Hooff, C. Buckens, M. Rutten, J. van Susante, R. Kroeze, M. de Kleuver, B. van Ginneken, N. Lessmann; Scientific Data, 2024;11(1):264.