31 citations · 95 across the 15 of their papers we have counts for
15 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…
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…
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
Philippe Weitz, Masi Valkonen, Leslie Solorzano +38
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability…
ACROBAT -- a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology
Philippe Weitz, Masi Valkonen, Leslie Solorzano +15
The analysis of FFPE tissue sections stained with haematoxylin and eosin (H&E) or immunohistochemistry (IHC) is an essential part of the pathologic assessment of surgically resecte…
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks
Philippe Weitz, Yinxi Wang, Kimmo Kartasalo +5
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics. However, molecular profiling remains costly and resource…
Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer
Yinxi Wang, Kimmo Kartasalo, Masi Valkonen +4
Molecular phenotyping is central in cancer precision medicine, but remains costly and standard methods only provide a tumour average profile. Microscopic morphological patterns obs…
Detection of Perineural Invasion in Prostate Needle Biopsies with Deep Neural Networks
Peter Ström, Kimmo Kartasalo, Pekka Ruusuvuori +6
Background: The detection of perineural invasion (PNI) by carcinoma in prostate biopsies has been shown to be associated with poor prognosis. The assessment and quantification of P…
Virtual reality for 3D histology: multi-scale visualization of organs with interactive feature exploration
Kaisa Liimatainen, Leena Latonen, Masi Valkonen +2
Virtual reality (VR) enables data visualization in an immersive and engaging manner, and it can be used for creating ways to explore scientific data. Here, we use VR for visualizat…
Pathologist-Level Grading of Prostate Biopsies with Artificial Intelligence
Peter Ström, Kimmo Kartasalo, Henrik Olsson +29
Background: An increasing volume of prostate biopsies and a world-wide shortage of uro-pathologists puts a strain on pathology departments. Additionally, the high intra- and inter-…