Zobrazeno 1 - 10
of 155
pro vyhledávání: '"Strand, Fredrik"'
Autor:
Garrucho, Lidia, Reidel, Claire-Anne, Kushibar, Kaisar, Joshi, Smriti, Osuala, Richard, Tsirikoglou, Apostolia, Bobowicz, Maciej, del Riego, Javier, Catanese, Alessandro, Gwoździewicz, Katarzyna, Cosaka, Maria-Laura, Abo-Elhoda, Pasant M., Tantawy, Sara W., Sakrana, Shorouq S., Shawky-Abdelfatah, Norhan O., Abdo-Salem, Amr Muhammad, Kozana, Androniki, Divjak, Eugen, Ivanac, Gordana, Nikiforaki, Katerina, Klontzas, Michail E., García-Dosdá, Rosa, Gulsun-Akpinar, Meltem, Lafcı, Oğuz, Mann, Ritse, Martín-Isla, Carlos, Prior, Fred, Marias, Kostas, Starmans, Martijn P. A., Strand, Fredrik, Díaz, Oliver, Igual, Laura, Lekadir, Karim
Current research in breast cancer Magnetic Resonance Imaging (MRI), especially with Artificial Intelligence (AI), faces challenges due to the lack of expert segmentations. To address this, we introduce the MAMA-MIA dataset, comprising 1506 multi-cent
Externí odkaz:
http://arxiv.org/abs/2406.13844
Autor:
Yeoh, Hong Hui, Liew, Andrea, Phan, Raphaël, Strand, Fredrik, Rahmat, Kartini, Nguyen, Tuong Linh, Hopper, John L., Tan, Maxine
Breast cancer is a significant public health concern and early detection is critical for triaging high risk patients. Sequential screening mammograms can provide important spatiotemporal information about changes in breast tissue over time. In this s
Externí odkaz:
http://arxiv.org/abs/2304.00257
Autor:
Karlsson, Jonathan, Strand, Fredrik, Bigun, Josef, Alonso-Fernandez, Fernando, Hernandez-Diaz, Kevin, Nilsson, Felix
Workplace injuries are common in today's society due to a lack of adequately worn safety equipment. A system that only admits appropriately equipped personnel can be created to improve working conditions. The goal is thus to develop a system that wil
Externí odkaz:
http://arxiv.org/abs/2212.04794
Autor:
Garrucho, Lidia, Kushibar, Kaisar, Osuala, Richard, Diaz, Oliver, Catanese, Alessandro, del Riego, Javier, Bobowicz, Maciej, Strand, Fredrik, Igual, Laura, Lekadir, Karim
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance since dense tissues can mask or even simulate masses. Therefore, the sensi
Externí odkaz:
http://arxiv.org/abs/2209.09809
Vision transformers have demonstrated the potential to outperform CNNs in a variety of vision tasks. But the computational and memory requirements of these models prohibit their use in many applications, especially those that depend on high-resolutio
Externí odkaz:
http://arxiv.org/abs/2208.07220
Autor:
Manigrasso, Francesco, Milazzo, Rosario, Russo, Alessandro Sebastian, Lamberti, Fabrizio, Strand, Fredrik, Pagnani, Andrea, Morra, Lia
Publikováno v:
In Medical Image Analysis January 2025 99
Autor:
Sorkhei, Moein, Liu, Yue, Azizpour, Hossein, Azavedo, Edward, Dembrower, Karin, Ntoula, Dimitra, Zouzos, Athanasios, Strand, Fredrik, Smith, Kevin
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. The missed screening-time detection is commonly
Externí odkaz:
http://arxiv.org/abs/2112.01330
Autor:
Bendazzoli, Simone, Brusini, Irene, Astaraki, Mehdi, Persson, Mats, Yu, Jimmy, Connolly, Bryan, Nyrén, Sven, Strand, Fredrik, Smedby, Örjan, Wang, Chunliang
Segmentation of COVID-19 lesions from chest CT scans is of great importance for better diagnosing the disease and investigating its extent. However, manual segmentation can be very time consuming and subjective, given the lesions' large variation in
Externí odkaz:
http://arxiv.org/abs/2012.14752
Autor:
Matsoukas, Christos, Hernandez, Albert Bou I, Liu, Yue, Dembrower, Karin, Miranda, Gisele, Konuk, Emir, Haslum, Johan Fredin, Zouzos, Athanasios, Lindholm, Peter, Strand, Fredrik, Smith, Kevin
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. This raises the question of whether this remains
Externí odkaz:
http://arxiv.org/abs/2008.00807
The ability to accurately estimate risk of developing breast cancer would be invaluable for clinical decision-making. One promising new approach is to integrate image-based risk models based on deep neural networks. However, one must take care when u
Externí odkaz:
http://arxiv.org/abs/2007.05791