Transi-Net: An Explainable Deep Learning Model Ensemble For Prostate's Transition Zone Segmentation

Συγγραφείς: Zaridis, Dimitrios I., Mylona, Eugenia, Tachos, Nikolaos S., Kalantzopoulos, Charalampos, Marias, Kostas, Tsiknakis, Manolis, Koutsouris, Dimitrios D., Matsopoulos, George K., Fotiadis, Dimitrios I.

Πηγή: 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE) BIBE Bioinformatics and Bioengineering (BIBE), 2023 IEEE 23rd International Conference on. :422-426 Dec, 2023

Relation: 2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)

Mask R-CNN kullanılarak yeni bir MRG veri tabanında prostat bölgelerinin segmentasyonu: PACS sistemi üzerinde bir uygulama

Συγγραφείς: Çağlar Gürkan, Abdulkadir Budak, Hakan Karataş, Kayıhan Akın

Πηγή: Volume: 39, Issue: 31401-1416
Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi
Journal of the Faculty of Engineering and Architecture of Gazi University

Θεματικοί όροι: Engineering, Prostat bölge segmentasyonu, prostat kanseri, T2W MRG, PI-RADS v2, derin öğrenme, Mühendislik, Prostate zone segmentation, prostate cancer, T2W MRI, deep learning

Περιγραφή αρχείου: application/pdf

Σύνδεσμος πρόσβασης: https://dergipark.org.tr/tr/pub/gazimmfd/issue/82783/1153507

Towards Patient-Individual PI-Rads v2 Sector Map: Cnn for Automatic Segmentation of Prostatic Zones From T2-Weighted MRI

Συγγραφείς: Meyer, Anneke, Rakr, Marko, Schindele, Daniel, Blaschke, Simon, Schostak, Martin, Fedorov, Andriy, Hansen, Christian

Πηγή: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019) Biomedical Imaging (ISBI 2019), 2019 IEEE 16th International Symposium on. :696-700 Apr, 2019

Relation: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI)

Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework /

Συγγραφείς: Vaitulevičius, Aleksas, Bernatavičienė, Jolita, Markevičiūtė, Jurgita, Naruševičiūtė, Ieva, Trakymas, Mantas, Treigys, Povilas

Πηγή: Machine learning: science and technology., Bristol : IOPScience, 2024, vol. 5, no. 4, art. no. 045003, p. [1-13]. ; ISSN 2632-2153 ; eISSN 2632-2153

Θεματικοί όροι: mpMRI, deep learning, artificial intelligence, prostate zone segmentation, computer vision

Περιγραφή αρχείου: application/pdf

Relation: https://epublications.vu.lt/object/elaba:209335806/209335806.pdf; https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US

Διαθεσιμότητα: https://repository.vu.lt/VU:ELABAPDB209335806&prefLang=en_US

Advancements in prostate zone segmentation: integrating attention mechanisms into the nnU-Net framework

Συγγραφείς: Aleksas Vaitulevičius, Jolita Bernatavičienė, Jurgita Markevičiutė, Ieva Naruševičiūtė, Mantas Trakymas, Povilas Treigys

Πηγή: Machine Learning: Science and Technology, Vol 5, Iss 4, p 045003 (2024)

Θεματικοί όροι: mpMRI, deep learning, artificial intelligence, prostate zone segmentation, computer vision, Computer engineering. Computer hardware, TK7885-7895, Electronic computers. Computer science, QA75.5-76.95

Relation: https://doi.org/10.1088/2632-2153/ad7f24; https://doaj.org/toc/2632-2153; https://doaj.org/article/7b4799afade34de3ad4433481963b495

Διαθεσιμότητα: https://doi.org/10.1088/2632-2153/ad7f24
https://doaj.org/article/7b4799afade34de3ad4433481963b495

Research and modeling of digital signals for the detection of cancerous areas of tissue in different MRI modalities / ; Skaitmeninių signalų tyrimas ir modeliavimas audinio vėžinėms zonoms aptikti skirtinguose MRT modalumuose.

Συγγραφείς: Vaitulevičius, Aleksas

Συνεισφορές: Treigys, Povilas

Θεματικοί όροι: mpMRI, artificial intelligence, prostate cancer segmentation, prostate zone segmentation, U-Net architecture

Περιγραφή αρχείου: application/pdf

Relation: https://epublications.vu.lt/object/elaba:323027223/323027223.pdf; https://repository.vu.lt/VU:ELABAETD323027223&prefLang=en_US

Διαθεσιμότητα: https://repository.vu.lt/VU:ELABAETD323027223&prefLang=en_US