Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning

Autor: Nguyen, Khanh, Nguyen, Huy Hoang, Panfilov, Egor, Tiulpin, Aleksei

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

SiNGR: Brain Tumor Segmentation via Signed Normalized Geodesic Transform Regression

Autor: Dang, Trung, Nguyen, Huy Hoang, Tiulpin, Aleksei

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

Beyond Classification: Definition and Density-based Estimation of Calibration in Object Detection

Autor: Popordanoska, Teodora, Tiulpin, Aleksei, Blaschko, Matthew B.

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

Understanding metric-related pitfalls in image analysis validation

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

Clinically-Inspired Multi-Agent Transformers for Disease Trajectory Forecasting from Multimodal Data

Autor: Nguyen, Huy Hoang, Blaschko, Matthew B., Saarakkala, Simo, Tiulpin, Aleksei

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