no code implementations • 15 Sep 2023 • Erlend Sortland Rolfsnes, Philip Thangngat, Trygve Eftestøl, Tobias Nordström, Fredrik Jäderling, Martin Eklund, Alvaro Fernandez-Quilez
Magnetic resonance imaging has evolved as a key component for prostate cancer (PCa) detection, substantially increasing the radiologist workload.
no code implementations • 12 Aug 2023 • Tim Nikolass Lindeijer, Tord Martin Ytredal, Trygve Eftestøl, Tobias Nordström, Fredrik Jäderling, Martin Eklund, Alvaro Fernandez-Quilez
Further, our approach shows good external volumetric generalization in an in-house dataset when tested with multi-view data (2. 76+-1. 89% compared to 3. 92+-3. 31%, P=. 002), showing the feasibility of exploiting non-annotated multi-view data through contrastive learning whilst providing flexibility at deployment in the event of missing views.
no code implementations • 10 Aug 2023 • Alvaro Fernandez-Quilez, Tobias Nordström, Fredrik Jäderling, Svein Reidar Kjosavik, Martin Eklund
Assessment: Chronological age was defined as the age of the participant at the time of the visit and used to train the deep learning model to predict the age of the patient.
no code implementations • 7 Jul 2023 • Xiaoyi Ji, Richard Salmon, Nita Mulliqi, Umair Khan, Yinxi Wang, Anders Blilie, Henrik Olsson, Bodil Ginnerup Pedersen, Karina Dalsgaard Sørensen, Benedicte Parm Ulhøi, Svein R Kjosavik, Emilius AM Janssen, Mattias Rantalainen, Lars Egevad, Pekka Ruusuvuori, Martin Eklund, Kimmo Kartasalo
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 performance and posing a challenge for widespread clinical application as fine-tuning algorithms for each new site is impractical.
no code implementations • 27 Jun 2021 • Bojing Liu, Yinxi Wang, Philippe Weitz, Johan Lindberg, Johan Hartman, Lars Egevad, Henrik Grönberg, Martin Eklund, Mattias Rantalainen
As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer.
1 code implementation • 19 Apr 2021 • Philippe Weitz, Yinxi Wang, Kimmo Kartasalo, Lars Egevad, Johan Lindberg, Henrik Grönberg, Martin Eklund, Mattias Rantalainen
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics.
no code implementations • 3 Apr 2020 • Peter Ström, Kimmo Kartasalo, Pekka Ruusuvuori, Henrik Grönberg, Hemamali Samaratunga, Brett Delahunt, Toyonori Tsuzuki, Lars Egevad, Martin Eklund
Results: For the detection of PNI in prostate biopsy cores the network had an estimated area under the receiver operating characteristics curve of 0. 98 (95% CI 0. 97-0. 99) based on 106 PNI positive cores and 1, 652 PNI negative cores in the independent test set.
no code implementations • 2 Jul 2019 • Peter Ström, Kimmo Kartasalo, Henrik Olsson, Leslie Solorzano, Brett Delahunt, Daniel M. Berney, David G. Bostwick, Andrew J. Evans, David J. Grignon, Peter A. Humphrey, Kenneth A. Iczkowski, James G. Kench, Glen Kristiansen, Theodorus H. van der Kwast, Katia R. M. Leite, Jesse K. McKenney, Jon Oxley, Chin-Chen Pan, Hemamali Samaratunga, John R. Srigley, Hiroyuki Takahashi, Toyonori Tsuzuki, Murali Varma, Ming Zhou, Johan Lindberg, Cecilia Bergström, Pekka Ruusuvuori, Carolina Wählby, Henrik Grönberg, Mattias Rantalainen, Lars Egevad, Martin Eklund
We additionally evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology.