3D Masked Modelling Advances Lesion Classification in Axial T2w Prostate MRI

no code implementations • 29 Dec 2022 • Alvaro Fernandez-Quilez, Christoffer Gabrielsen Andersen, Trygve Eftestøl, Svein Reidar Kjosavik, Ketil Oppedal

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 of a large amount of unlabelled natural images.

Lesion Classification Self-Supervised Learning

Self-transfer learning via patches: A prostate cancer triage approach based on bi-parametric MRI

no code implementations • 22 Jul 2021 • Alvaro Fernandez-Quilez, Trygve Eftestøl, Morten Goodwin, Svein Reidar Kjosavik, Ketil Oppedal

Nevertheless, they rely on large amounts of annotated data which is not common in the medical field.

Transfer Learning

Improving prostate whole gland segmentation in t2-weighted MRI with synthetically generated data

no code implementations • 27 Mar 2021 • Alvaro Fernandez-Quilez, Steinar Valle Larsen, Morten Goodwin, Thor Ole Gulsurd, Svein Reidar Kjosavik, Ketil Oppedal

Whole gland (WG) segmentation of the prostate plays a crucial role in detection, staging and treatment planning of prostate cancer (PCa).

Data Augmentation Translation