Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Kainmüller, Dagmar"'
Autor:
Franzen, Jannik, Winklmayr, Claudia, Guarino, Vanessa E., Karg, Christoph, Yu, Xiaoyan, Koreuber, Nora, Albrecht, Jan P., Bischoff, Philip, Kainmueller, Dagmar
Uncertainty Quantification (UQ) is crucial for reliable image segmentation. Yet, while the field sees continual development of novel methods, a lack of agreed-upon benchmarks limits their systematic comparison and evaluation: Current UQ methods are t
Externí odkaz:
http://arxiv.org/abs/2411.07097
Heatmaps generated on inputs of image classification networks via explainable AI methods like Grad-CAM and LRP have been observed to resemble segmentations of input images in many cases. Consequently, heatmaps have also been leveraged for achieving w
Externí odkaz:
http://arxiv.org/abs/2407.03009
Autor:
Mais, Lisa, Hirsch, Peter, Managan, Claire, Kandarpa, Ramya, Rumberger, Josef Lorenz, Reinke, Annika, Maier-Hein, Lena, Ihrke, Gudrun, Kainmueller, Dagmar
Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said
Externí odkaz:
http://arxiv.org/abs/2404.00130
The classification of gigapixel histopathology images with deep multiple instance learning models has become a critical task in digital pathology and precision medicine. In this work, we propose a Transformer-based multiple instance learning approach
Externí odkaz:
http://arxiv.org/abs/2308.12634
Autor:
Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Weigert, Martin, Schmidt, Uwe, Zhang, Wenhua, Zhang, Jun, Yang, Sen, Xiang, Jinxi, Wang, Xiyue, Rumberger, Josef Lorenz, Baumann, Elias, Hirsch, Peter, Liu, Lihao, Hong, Chenyang, Aviles-Rivero, Angelica I., Jain, Ayushi, Ahn, Heeyoung, Hong, Yiyu, Azzuni, Hussam, Xu, Min, Yaqub, Mohammad, Blache, Marie-Claire, Piégu, Benoît, Vernay, Bertrand, Scherr, Tim, Böhland, Moritz, Löffler, Katharina, Li, Jiachen, Ying, Weiqin, Wang, Chixin, Kainmueller, Dagmar, Schönlieb, Carola-Bibiane, Liu, Shuolin, Talsania, Dhairya, Meda, Yughender, Mishra, Prakash, Ridzuan, Muhammad, Neumann, Oliver, Schilling, Marcel P., Reischl, Markus, Mikut, Ralf, Huang, Banban, Chien, Hsiang-Chin, Wang, Ching-Ping, Lee, Chia-Yen, Lin, Hong-Kun, Liu, Zaiyi, Pan, Xipeng, Han, Chu, Cheng, Jijun, Dawood, Muhammad, Deshpande, Srijay, Bashir, Raja Muhammad Saad, Shephard, Adam, Costa, Pedro, Nunes, João D., Campilho, Aurélio, Cardoso, Jaime S., S, Hrishikesh P, Puthussery, Densen, G, Devika R, C V, Jiji, Zhang, Ye, Fang, Zijie, Lin, Zhifan, Zhang, Yongbing, Lin, Chunhui, Zhang, Liukun, Mao, Lijian, Wu, Min, Vo, Vi Thi-Tuong, Kim, Soo-Hyung, Lee, Taebum, Kondo, Satoshi, Kasai, Satoshi, Dumbhare, Pranay, Phuse, Vedant, Dubey, Yash, Jamthikar, Ankush, Vuong, Trinh Thi Le, Kwak, Jin Tae, Ziaei, Dorsa, Jung, Hyun, Miao, Tianyi, Snead, David, Raza, Shan E Ahmed, Minhas, Fayyaz, Rajpoot, Nasir M.
Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest
Externí odkaz:
http://arxiv.org/abs/2303.06274
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:
Hirsch, Peter, Malin-Mayor, Caroline, Santella, Anthony, Preibisch, Stephan, Kainmueller, Dagmar, Funke, Jan
Tracking all nuclei of an embryo in noisy and dense fluorescence microscopy data is a challenging task. We build upon a recent method for nuclei tracking that combines weakly-supervised learning from a small set of nuclei center point annotations wit
Externí odkaz:
http://arxiv.org/abs/2208.11467
Autor:
Haller, Stefan, Feineis, Lorenz, Hutschenreiter, Lisa, Bernard, Florian, Rother, Carsten, Kainmüller, Dagmar, Swoboda, Paul, Savchynskyy, Bogdan
The graph matching optimization problem is an essential component for many tasks in computer vision, such as bringing two deformable objects in correspondence. Naturally, a wide range of applicable algorithms have been proposed in the last decades. S
Externí odkaz:
http://arxiv.org/abs/2207.00291
Autor:
Maier-Hein, Lena, Reinke, Annika, Godau, Patrick, Tizabi, Minu D., Buettner, Florian, Christodoulou, Evangelia, Glocker, Ben, Isensee, Fabian, Kleesiek, Jens, Kozubek, Michal, Reyes, Mauricio, Riegler, Michael A., Wiesenfarth, Manuel, Kavur, A. Emre, Sudre, Carole H., Baumgartner, Michael, Eisenmann, Matthias, Heckmann-Nötzel, Doreen, Rädsch, Tim, Acion, Laura, Antonelli, Michela, Arbel, Tal, Bakas, Spyridon, Benis, Arriel, Blaschko, Matthew, Cardoso, M. Jorge, Cheplygina, Veronika, Cimini, Beth A., Collins, Gary S., Farahani, Keyvan, Ferrer, Luciana, Galdran, Adrian, van Ginneken, Bram, Haase, Robert, Hashimoto, Daniel A., Hoffman, Michael M., Huisman, Merel, Jannin, Pierre, Kahn, Charles E., Kainmueller, Dagmar, Kainz, Bernhard, Karargyris, Alexandros, Karthikesalingam, Alan, Kenngott, Hannes, Kofler, Florian, Kopp-Schneider, Annette, 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, Rajpoot, Nasir, 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, Jäger, Paul F.
Publikováno v:
Nature methods, 1-18 (2024)
Increasing evidence shows that flaws in machine learning (ML) algorithm validation are an underestimated global problem. Particularly in automatic biomedical image analysis, chosen performance metrics often do not reflect the domain interest, thus fa
Externí odkaz:
http://arxiv.org/abs/2206.01653
Autor:
Rumberger, Josef Lorenz, Baumann, Elias, Hirsch, Peter, Janowczyk, Andrew, Zlobec, Inti, Kainmueller, Dagmar
We describe here the panoptic segmentation method we devised for our participation in the CoNIC: Colon Nuclei Identification and Counting Challenge at ISBI 2022. Key features of our method are a weighted loss specifically engineered for semantic segm
Externí odkaz:
http://arxiv.org/abs/2203.11692