3 citations · 8 across the 9 of their papers we have counts for
9 papers
Validation of an AI-based end-to-end model for prostate pathology using long-term archived routine samples
Xiaoyi Ji, Renata Zelic, Oskar Aspegren +11
Artificial intelligence (AI) is becoming a clinical tool for prostate pathology, but generalization across variations in sample preparation and preservation over prolonged time per…
AI-based Prediction of Biochemical Recurrence from Biopsy and Prostatectomy Samples
Andrea Camilloni, Chiara Micoli, Nita Mulliqi +11
Biochemical recurrence (BCR) after radical prostatectomy (RP) is a surrogate marker for aggressive prostate cancer with adverse outcomes, yet current prognostic tools remain imprec…
Validation of Diagnostic Artificial Intelligence Models for Prostate Pathology in a Middle Eastern Cohort
Peshawa J. Muhammad Ali, Navin Vincent, Saman S. Abdulla +8
Background: Artificial intelligence (AI) is improving the efficiency and accuracy of cancer diagnostics. The performance of pathology AI systems has been almost exclusively evaluat…
The PAR dataset: Prostate biopsy whole slide images from an underrepresented Middle Eastern population
Peshawa J. Muhammad Ali, Navin Vincent, Saman S. Abdulla +8
Artificial intelligence (AI) is increasingly used in digital pathology. Publicly available histopathology datasets remain scarce, and those that do exist predominantly represent We…
Finding Holes: Pathologist Level Performance Using AI for Cribriform Morphology Detection in Prostate Cancer
Kelvin Szolnoky, Anders Blilie, Nita Mulliqi +23
Background: Cribriform morphology in prostate cancer is a histological feature that indicates poor prognosis and contraindicates active surveillance. However, it remains underrepor…
Artificial Intelligence-Assisted Prostate Cancer Diagnosis for Reduced Use of Immunohistochemistry
Anders Blilie, Nita Mulliqi, Xiaoyi Ji +13
Prostate cancer diagnosis heavily relies on histopathological evaluation, which is subject to variability. While immunohistochemical staining (IHC) assists in distinguishing benign…
The impact of tissue detection on diagnostic artificial intelligence algorithms in digital pathology
Sol Erika Boman, Nita Mulliqi, Anders Blilie +18
Tissue detection is a crucial first step in most digital pathology applications. Details of the segmentation algorithm are rarely reported, and there is a lack of studies investiga…
Foundation Models -- A Panacea for Artificial Intelligence in Pathology?
Nita Mulliqi, Anders Blilie, Xiaoyi Ji +28
The role of artificial intelligence (AI) in pathology has evolved from aiding diagnostics to uncovering predictive morphological patterns in whole slide images (WSIs). Recently, fo…
Physical Color Calibration of Digital Pathology Scanners for Robust Artificial Intelligence Assisted Cancer Diagnosis
Xiaoyi Ji, Richard Salmon, Nita Mulliqi +14
The potential of artificial intelligence (AI) in digital pathology is limited by technical inconsistencies in the production of whole slide images (WSIs), leading to degraded AI pe…