dr. Görkem Saygili PhD

Nápoles, G., Concepción, L., Özgöde Yigin, B., Saygili, G., Vanhoof, K., & Bello, R. (2024). Weighted t-Distributed Stochastic Neighbor Embedding for Projection-Based Clustering. In Y. Hernández Heredia, V. Milián Núñez, & J. Ruiz Shulcloper (Eds.), Progress in Artificial Intelligence and Pattern Recognition - 8th International Congress on Artificial Intelligence and Pattern Recognition, IWAIPR 2023, Proceedings (pp. 131-142). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14335 LNCS).

Jadikar, M., van Leeuwen, M., van Oudheusden, T., Oei, S., Steunenberg, B., Kint, R., Ranschaert, E., Bosma, G., Saygili, G., & Ong, S. (2023). Deep Learning Classifiers to Reduce False Positives in Osteolytic Lesion Segmentation Results from Low-dose CT Scans of Multiple Myeloma. Paper presented at 35rd Benelux Conference on Artificial Intelligence and the 32th Belgian Dutch Conference on Machine Learning, Delft , Netherlands. https://bnaic2023.tudelft.nl/static/media/BNAICBENELEARN_2023_paper_65.7e5de9cf01a9bf3f4bc8.pdf

Özgöde Yigin, B., & Saygili, G. (2023). Effect of distance measures on confidences of t-SNE embeddings and its implications on clustering for scRNA-seq data. Scientific Reports, 13, Article 6567.

Saygili, G., & Özgöde Yigin, B. (2023). Continual learning approaches for single cell RNA sequencing data. Scientific Reports, 13, Article 15286.

Ong, S., van Leeuwen, M., Saygili, G., Hoff , W., Heres, M., van Oudheusden, T., Steunenberg, B., Kint, R., Bosma, G., & Ranschaert, E. (2022). A deep learning-based approach to detect and segment osteolytic bone lesions in whole-body, low-dose CT imaging of multiple myeloma patients. Poster session presented at EuSoMII Annual Meeting 2022 ‘Your portal to AI’, Valencia, Spain.