2 documents found, page 1 of 1
Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressivene...
Rodrigues, Nuno M.; Almeida, José Guilherme de; Rodrigues, Ana; Vanneschi, Leonardo; Matos, Celso; Lisitskaya, Maria; Uysal, Aycan; Silva, SaraRodrigues, N. M., Almeida, J. G. D., Rodrigues, A., Vanneschi, L., Matos, C., Lisitskaya, M., Uysal, A., Silva, S., & Papanikolaou, N. (2024). Deep Learning Features Can Improve Radiomics-Based Prostate Cancer Aggressiveness Prediction. JCO Clinical Cancer Informatics, 8, Article e2300180. https://doi.org/10.1200/CCI.23.00180 --- Supported in part by FCT, Portugal, through funding of the LASIGE Research Unit (U...
Quantification of tumor burden in multiple myeloma by atlas-based semi-automati...
Almeida, Sílvia D.; Santinha, João; Oliveira, Francisco P.M.; Ip, Joana; Lisitskaya, Maria; Lourenço, João; Uysal, Aycan; Matos, Celso; João, CristinaBACKGROUND: Whole-body diffusion weighted imaging (WB-DWI) has proven value to detect multiple myeloma (MM) lesions. However, the large volume of imaging data and the presence of numerous lesions makes the reading process challenging. The aim of the current study was to develop a semi-automatic lesion segmentation algorithm for WB-DWI images in MM patients and to evaluate this smart-algorithm (SA) performance b...