Data-efficient federated semi-supervised learning framework via pseudo supervision refinement strategy for lung tumor segmentation. - Li, Weixing; Pan, Xipeng; Zhang, Zhen; Wu, Guangyao; Ye, Guanchao; Wee, Leonard; Dekker, Andre; Han, Chu; Shi, Lei; Liu, Zaiyi; Liu, Zhenbing; Shi, Zhenwei (Biomed. Signal Process. Control., 2025)

Label-efficient transformer-based framework with self-supervised strategies for heterogeneous lung tumor segmentation. - Liu, Zhenbing; Li, Weixing; Cui, Yanfen; Chen, Xin; Pan, Xipeng; Ye, Guanchao; Wu, Guangyao; Liao, Yongde; Volmer, Leroy; Wee, Leonard; Dekker, Andre; Han, Chu; et al. (Expert Syst. Appl., 2025)

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation. - Zhao, Zhihe; Du, Siyao; Xu, Zeyan; Yin, Zhi; Huang, Xiaomei; Huang, Xin; Wong, Chinting; Liang, Yanting; Shen, Jing; Wu, Jianlin; Qu, Jinrong; Zhang, Lina; et al. (Comput. Biol. Medicine, 2024)

Systematic review and meta-analysis of prediction models used in cervical cancer. - Jha, Ashish Kumar; Mithun, Sneha; Sherkhane, Umeshkumar B.; Jaiswar, Vinay; Osong, Biche; Purandare, Nilendu; Kannan, Sadhana; Prabhash, Kumar; Gupta, Sudeep; Vanneste, Ben G. L.; Rangarajan, Venkatesh; Dekker, Andre; et al. (Artif. Intell. Medicine, 2023)

Are all shortcuts in encoder-decoder networks beneficial for CT denoising? - Chen, Junhua; Zhang, Chong; Wee, Leonard; Dekker, Andre; Bermejo, Iñigo (Comput. methods Biomech. Biomed. Eng. Imaging Vis., 2023)

Clinical Concept-Based Radiology Reports Classification Pipeline for Lung Carcinoma. - Mithun, Sneha; Jha, Ashish Kumar; Sherkhane, Umeshkumar B.; Jaiswar, Vinay; Purandare, Nilendu; Dekker, Andre; Puts, Sander; Bermejo, Iñigo; Rangarajan, Venkatesh; Zegers, Catharina M. L.; Wee, Leonard (J. Digit. Imaging, 2023)

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer. - Jha, Ashish Kumar; Sherkhane, Umeshkumar B.; Mithun, Sneha; Jaiswar, Vinay; Purandare, Nilendu; Prabhash, Kumar; Wee, Leonard; Rangarajan, Venkatesh; Dekker, Andre (J. Digit. Imaging, 2023)

GAN-based one dimensional medical data augmentation. - Zhang, Ye; Wang, Zhixiang; Zhang, Zhen; Liu, Junzhuo; Feng, Ying; Wee, Leonard; Dekker, Andre; Chen, Qiaosong; Traverso, Alberto (Soft Comput., 2023)

Privacy-Preserving Dashboard for F.A.I.R Head and Neck Cancer data supporting multi-centered collaborations. - Gouthamchand, Varsha; Choudhury, Ananya; Hoebers, Frank; Wesseling, Frederik; Welch, Mattea; Kim, Sejin; Haibe-Kains, Benjamin; Kazmierska, Joanna; Dekker, Andre; Soest, Johan van; Wee, Leonard (2023)

Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark. - Afshar, Parnian; Mohammadi, Arash; Plataniotis, Konstantinos N.; Farahani, Keyvan; Kirby, Justin S.; Oikonomou, Anastasia; Asif, Amir; Wee, Leonard; Dekker, Andre; Wu, Xin; Haque, Mohammad Ariful; Hossain, Shahruk; et al. (CoRR, 2022)

Deep Learning Automated Segmentation for Muscle and Adipose Tissue from Abdominal Computed Tomography in Polytrauma Patients. - Ackermans, Leanne L. G. C.; Volmer, Leroy; Wee, Leonard; Brecheisen, Ralph; Sánchez-González, Patricia; Seiffert, Alexander P.; Gómez, Enrique J.; Dekker, Andre; Bosch, Jan A. Ten; Damink, Steven M. W. Olde; Blokhuis, Taco J. (Sensors, 2021)

Lung Cancer Diagnosis Using Deep Attention Based on Multiple Instance Learning and Radiomics. - Chen, Junhua; Zeng, Haiyan; Zhang, Chong; Shi, Zhenwei; Dekker, Andre; Wee, Leonard; Bermejo, Iñigo (CoRR, 2021)

Generative Models Improve Radiomics Reproducibility in Low Dose CTs: A Simulation Study. - Chen, Junhua; Zhang, Chong; Traverso, Alberto; Zhovannik, Ivan; Dekker, Andre; Wee, Leonard; Bermejo, Iñigo (CoRR, 2021)

Generative Models Improve Radiomics Performance in Different Tasks and Different Datasets: An Experimental Study. - Chen, Junhua; Bermejo, Iñigo; Dekker, Andre; Wee, Leonard (CoRR, 2021)

Personalized risk prediction for breast cancer pre-screening using artificial intelligence and thermal radiomics. - Kakileti, Siva Teja; Madhu, Himanshu J.; Manjunath, Geetha; Wee, Leonard; Dekker, Andre; Sampangi, Sudhakar (Artif. Intell. Medicine, 2020)

Auto Segmentation of Lung in Non-small Cell Lung Cancer Using Deep Convolution Neural Network. - Patil, Ravindra; Wee, Leonard; Dekker, Andre (2020)

Non-invasive prediction of lymph node risk in oral cavity cancer patients using a combination of supervised and unsupervised machine learning algorithms. - Traverso, Alberto; Hosni-Abdalaty, Ali; Hasan, Mohammad; Tadic, Tony; Patel, Tirth; Giuliani, Meredith; Kim, John; Ringash, Jolie; Cho, John; Bratman, Scott V.; Bayley, Andrew; Waldron, John; et al. (2020)

A Feature-Pooling and Signature-Pooling Method for Feature Selection for Quantitative Image Analysis: Application to a Radiomics Model for Survival in Glioma. - Shi, Zhenwei; Zhang, Chong; Compter, Inge; Verduin, Maikel; Hoeben, Ann; Eekers, Danielle; Dekker, Andre; Wee, Leonard (2019)

FAIR Quantitative Imaging in Oncology: how Semantic Web and Ontologies will support Reproducible Science. - Traverso, Alberto; Shi, Zhenwei; Wee, Leonard; Dekker, Andre (2019)

Publishing Linked and FAIR-compliant Radiomics Data in Radiation Oncology via Ontologies and Semantic Web Techniques. - Traverso, Alberto; Vallières, Martin; Soest, Johan van; Wee, Leonard; Morin, Olivier; Dekker, Andre (2019)