Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Supasan Sripodok"'
Autor:
Narongrid Seesawad, Piyalitt Ittichaiwong, Thapanun Sudhawiyangkul, Phattarapong Sawangjai, Peti Thuwajit, Paisarn Boonsakan, Supasan Sripodok, Kanyakorn Veerakanjana, Komgrid Charngkaew, Ananya Pongpaibul, Napat Angkathunyakul, Narit Hnoohom, Sumeth Yuenyong, Chanitra Thuwajit, Theerawit Wilaiprasitporn
Publikováno v:
IEEE Open Journal of Engineering in Medicine and Biology, Vol 5, Pp 514-523 (2024)
Background: Deep learning models for patch classification in whole-slide images (WSIs) have shown promise in assisting follicular lymphoma grading. However, these models often require pathologists to identify centroblasts and manually provide refined
Externí odkaz:
https://doaj.org/article/0c94c612c58b45a59a13709fb48791dd
Autor:
Sumeth Yuenyong, Paisarn Boonsakan, Supasan Sripodok, Peti Thuwajit, Komgrid Charngkaew, Ananya Pongpaibul, Napat Angkathunyakul, Narit Hnoohom, Chanitra Thuwajit
Publikováno v:
Frontiers in Medicine, Vol 11 (2024)
IntroductionDetection and counting of Centroblast cells (CB) in hematoxylin & eosin (H&E) stained whole slide image (WSI) is an important workflow in grading Lymphoma. Each high power field (HPF) patch of a WSI is inspected for the number of CB cells
Externí odkaz:
https://doaj.org/article/facc13b7ac3549eab0583841bfc8952c
Autor:
Supasan Sripodok, Nontawat Benjakul
Publikováno v:
Annals of Diagnostic Pathology. 63:152083
Autor:
Nontawat Benjakul, Supasan Sripodok, Tanapong Tungjitsirisun, Pattara Pattaravoratham, Thararat Soiphet
Vajira Medical Journal, 65, 6, 501-512
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f216aeb8969afd6d33facf90784cb720