179 citations · 200 across the 5 of their papers we have counts for
5 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…
3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI
Alvaro Fernandez-Quilez, Christoffer Gabrielsen Andersen, Trygve Eftestøl +2
Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence o…
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…
The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
Gustav Mårtensson, Daniel Ferreira, Tobias Granberg +22
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in…