no code implementations • 19 Feb 2024 • Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo
In this article, we explore the use of federated ensembles of Bayesian networks (FBNE) in a range of experiments and compare their performance with locally trained models and models trained with VertiBayes, a federated learning algorithm to train Bayesian networks from decentralized data.
1 code implementation • 31 Oct 2022 • Florian van Daalen, Lianne Ippel, Andre Dekker, Inigo Bermejo
For structure learning we adapted the widely used K2 algorithm with a privacy-preserving scalar product protocol.
no code implementations • 20 Jan 2022 • P. M. A van Ooijen, Erfan Darzidehkalani, Andre Dekker
Artificial intelligence (AI), especially deep learning, requires vast amounts of data for training, testing, and validation.
no code implementations • 3 Jan 2022 • Parnian Afshar, Arash Mohammadi, Konstantinos N. Plataniotis, Keyvan Farahani, Justin Kirby, Anastasia Oikonomou, Amir Asif, Leonard Wee, Andre Dekker, Xin Wu, Mohammad Ariful Haque, Shahruk Hossain, Md. Kamrul Hasan, Uday Kamal, Winston Hsu, Jhih-Yuan Lin, M. Sohel Rahman, Nabil Ibtehaz, Sh. M. Amir Foisol, Kin-Man Lam, Zhong Guang, Runze Zhang, Sumohana S. Channappayya, Shashank Gupta, Chander Dev
Lung cancer is one of the deadliest cancers, and in part its effective diagnosis and treatment depend on the accurate delineation of the tumor.
no code implementations • 17 Dec 2021 • Florian van Daalen, Inigo Bermejo, Lianne Ippel, Andre Dekker
Privacy-preserving machine learning enables the training of models on decentralized datasets without the need to reveal the data, both on horizontal and vertically partitioned data.
1 code implementation • 1 Oct 2021 • Hajar Hasannejadasl, Cheryl Roumen, Henk van der Poel, Ben Vanneste, Joep van Roermund, Katja Aben, Petros Kalendralis, Biche Osong, Lambertus Kiemeney, Inge Van Oort, Renee Verwey, Laura Hochstenbach, Esther J. Bloemen- van Gurp, Andre Dekker, Rianne R. R. Fijten
While the 10-year survival rate for localized prostate cancer patients is very good (>98%), side effects of treatment may limit quality of life significantly.
no code implementations • 16 Sep 2021 • Junhua Chen, Leonard Wee, Andre Dekker, Inigo Bermejo
The trained GANs were applied to three scenarios: 1) improving radiomics reproducibility in simulated low dose CT images and 2) same-day repeat low dose CTs (RIDER dataset) and 3) improving radiomics performance in survival prediction.
no code implementations • 6 Sep 2021 • Junhua Chen, Inigo Bermejo, Andre Dekker, Leonard Wee
Generative models can improve the performance of low dose CT-based radiomics in different tasks.
1 code implementation • 30 Apr 2021 • Junhua Chen, Chong Zhang, Alberto Traverso, Ivan Zhovannik, Andre Dekker, Leonard Wee, Inigo Bermejo
Moreover, images with different noise levels can be denoised to improve the reproducibility using these models without re-training, as long as the noise intensity is equal or lower than that in high-noise CTs.
no code implementations • 29 Apr 2021 • Junhua Chen, Haiyan Zeng, Chong Zhang, Zhenwei Shi, Andre Dekker, Leonard Wee, Inigo Bermejo
In this article, we treat lung cancer diagnosis as a multiple instance learning (MIL) problem in order to better reflect the diagnosis process in the clinical setting and for the higher interpretability of the output.