Zobrazeno 1 - 10
of 135
pro vyhledávání: '"Tiulpin, Aleksei"'
Mamba, a State Space Model (SSM), has recently shown competitive performance to Convolutional Neural Networks (CNNs) and Transformers in Natural Language Processing and general sequence modeling. Various attempts have been made to adapt Mamba to Comp
Externí odkaz:
http://arxiv.org/abs/2408.14415
Osteoarthritis (OA) is the most common musculoskeletal disease, which has no cure. Knee OA (KOA) is one of the highest causes of disability worldwide, and it costs billions of United States dollars to the global community. Prediction of KOA progressi
Externí odkaz:
http://arxiv.org/abs/2408.02349
Accurate retinal vessel (RV) segmentation is a crucial step in the quantitative assessment of retinal vasculature, which is needed for the early detection of retinal diseases and other conditions. Numerous studies have been conducted to tackle the pr
Externí odkaz:
http://arxiv.org/abs/2405.16815
One of the primary challenges in brain tumor segmentation arises from the uncertainty of voxels close to tumor boundaries. However, the conventional process of generating ground truth segmentation masks fails to treat such uncertainties properly. Tho
Externí odkaz:
http://arxiv.org/abs/2405.16813
Autor:
Popordanoska, Teodora, Gruber, Sebastian G., Tiulpin, Aleksei, Buettner, Florian, Blaschko, Matthew B.
Proper scoring rules evaluate the quality of probabilistic predictions, playing an essential role in the pursuit of accurate and well-calibrated models. Every proper score decomposes into two fundamental components -- proper calibration error and ref
Externí odkaz:
http://arxiv.org/abs/2312.08589
Despite their impressive predictive performance in various computer vision tasks, deep neural networks (DNNs) tend to make overly confident predictions, which hinders their widespread use in safety-critical applications. While there have been recent
Externí odkaz:
http://arxiv.org/abs/2312.06645
Knee Osteoarthritis (KOA) is a highly prevalent chronic musculoskeletal condition with no currently available treatment. The manifestation of KOA is heterogeneous and prediction of its progression is challenging. Current literature suggests that the
Externí odkaz:
http://arxiv.org/abs/2307.00873
Autor:
Reinke, Annika, Tizabi, Minu D., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Kavur, A. Emre, Rädsch, Tim, Sudre, Carole H., Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Buettner, Florian, Cardoso, M. Jorge, Cheplygina, Veronika, Chen, Jianxu, Christodoulou, Evangelia, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Glocker, Ben, Godau, Patrick, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Isensee, Fabian, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kleesiek, Jens, Kofler, Florian, Kooi, Thijs, Kopp-Schneider, Annette, Kozubek, Michal, Kreshuk, Anna, Kurc, Tahsin, Landman, Bennett A., Litjens, Geert, Madani, Amin, Maier-Hein, Klaus, Martel, Anne L., Mattson, Peter, Meijering, Erik, Menze, Bjoern, Moons, Karel G. M., Müller, Henning, Nichyporuk, Brennan, Nickel, Felix, Petersen, Jens, Rafelski, Susanne M., Rajpoot, Nasir, Reyes, Mauricio, Riegler, Michael A., Rieke, Nicola, Saez-Rodriguez, Julio, Sánchez, Clara I., Shetty, Shravya, van Smeden, Maarten, Summers, Ronald M., Taha, Abdel A., Tiulpin, Aleksei, Tsaftaris, Sotirios A., Van Calster, Ben, Varoquaux, Gaël, Wiesenfarth, Manuel, Yaniv, Ziv R., Jäger, Paul F., Maier-Hein, Lena
Publikováno v:
Nature methods, 1-13 (2024)
Validation metrics are key for the reliable tracking of scientific progress and for bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in im
Externí odkaz:
http://arxiv.org/abs/2302.01790
Autor:
Kowlagi, Narasimharao, Nguyen, Huy Hoang, McSweeney, Terence, Saarakkala, Simo, määttä, Juhani, Karppinen, Jaro, Tiulpin, Aleksei
This paper addresses the challenge of grading visual features in lumbar spine MRI using Deep Learning. Such a method is essential for the automatic quantification of structural changes in the spine, which is valuable for understanding low back pain.
Externí odkaz:
http://arxiv.org/abs/2210.14597
Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data
Deep neural networks are often applied to medical images to automate the problem of medical diagnosis. However, a more clinically relevant question that practitioners usually face is how to predict the future trajectory of a disease. Current methods
Externí odkaz:
http://arxiv.org/abs/2210.13889