5 citations · 5 across the 3 of their papers we have counts for
4 papers
Out-of-distribution multi-view auto-encoders for prostate cancer lesion detection
Alvaro Fernandez-Quilez, Linas Vidziunas, Ørjan Kløvfjell Thoresen +3
Traditional deep learning (DL) approaches based on supervised learning paradigms require large amounts of annotated data that are rarely available in the medical domain. Unsupervis…
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…
Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI
Alvaro Fernandez-Quilez, Trygve Eftestøl, Morten Goodwin +2
Prostate cancer (PCa) is the second most common cancer diagnosed among men worldwide. The current PCa diagnostic pathway comes at the cost of substantial overdiagnosis, leading to…
Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data
Alvaro Fernandez-Quilez, Steinar Valle Larsen, Morten Goodwin +3
Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa). Despite promise shown by deep learning (DL…