173 citations · 237 across the 9 of their papers we have counts for
9 papers
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
Tim Nikolass Lindeijer, Tord Martin Ytredal, Trygve Eftestøl +4
An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools conside…
On undesired emergent behaviors in compound prostate cancer detection systems
Erlend Sortland Rolfsnes, Philip Thangngat, Trygve Eftestøl +4
Artificial intelligence systems show promise to aid in the di- agnostic pathway of prostate cancer (PC), by supporting radiologists in interpreting magnetic resonance images (MRI)…
Prostate Age Gap (PAG): An MRI surrogate marker of aging for prostate cancer detection
Alvaro Fernandez-Quilez, Tobias Nordström, Fredrik Jäderling +2
Background: Prostate cancer (PC) MRI-based risk calculators are commonly based on biological (e.g. PSA), MRI markers (e.g. volume), and patient age. Whilst patient age measures the…
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…
Using deep learning to detect patients at risk for prostate cancer despite benign biopsies
Bojing Liu, Yinxi Wang, Philippe Weitz +6
Background: Transrectal ultrasound guided systematic biopsies of the prostate is a routine procedure to establish a prostate cancer diagnosis. However, the 10-12 prostate core biop…
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…
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…
Artificial Intelligence Assistance Significantly Improves Gleason Grading of Prostate Biopsies by Pathologists
Wouter Bulten, Maschenka Balkenhol, Jean-Joël Awoumou Belinga +17
While the Gleason score is the most important prognostic marker for prostate cancer patients, it suffers from significant observer variability. Artificial Intelligence (AI) systems…
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-…