no code implementations • 1 Apr 2023 • Hong Hui Yeoh, Andrea Liew, Raphaël Phan, Fredrik Strand, Kartini Rahmat, Tuong Linh Nguyen, John L. Hopper, Maxine Tan
Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time.
no code implementations • 9 Dec 2022 • Jonathan Karlsson, Fredrik Strand, Josef Bigun, Fernando Alonso-Fernandez, Kevin Hernandez-Diaz, Felix Nilsson
Workplace injuries are common in today's society due to a lack of adequately worn safety equipment.
1 code implementation • 20 Sep 2022 • Lidia Garrucho, Kaisar Kushibar, Richard Osuala, Oliver Diaz, Alessandro Catanese, Javier del Riego, Maciej Bobowicz, Fredrik Strand, Laura Igual, Karim Lekadir
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection.
1 code implementation • 10 Aug 2022 • Yue Liu, Christos Matsoukas, Fredrik Strand, Hossein Azizpour, Kevin Smith
This simple approach, PatchDropout, reduces FLOPs and memory by at least 50% in standard natural image datasets such as ImageNet, and those savings only increase with image size.
2 code implementations • 2 Dec 2021 • Moein Sorkhei, Yue Liu, Hossein Azizpour, Edward Azavedo, Karin Dembrower, Dimitra Ntoula, Athanasios Zouzos, Fredrik Strand, Kevin Smith
Interval and large invasive breast cancers, which are associated with worse prognosis than other cancers, are usually detected at a late stage due to false negative assessments of screening mammograms.
no code implementations • 29 Dec 2020 • Simone Bendazzoli, Irene Brusini, Mehdi Astaraki, Mats Persson, Jimmy Yu, Bryan Connolly, Sven Nyrén, Fredrik Strand, Örjan Smedby, Chunliang Wang
Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent.
2 code implementations • ICML 2020 • Christos Matsoukas, Albert Bou I Hernandez, Yue Liu, Karin Dembrower, Gisele Miranda, Emir Konuk, Johan Fredin Haslum, Athanasios Zouzos, Peter Lindholm, Fredrik Strand, Kevin Smith
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.
1 code implementation • 11 Jul 2020 • Yue Liu, Hossein Azizpour, Fredrik Strand, Kevin Smith
With this in mind, we trained networks using three different criteria to select the positive training data (i. e. images from patients that will develop cancer): an inherent risk model trained on images with no visible signs of cancer, a cancer signs model trained on images containing cancer or early signs of cancer, and a conflated model trained on all images from patients with a cancer diagnosis.