Zobrazeno 1 - 10
of 4 151
pro vyhledávání: '"Zimmerer, A."'
Autor:
Wald, Tassilo, Ulrich, Constantin, Köhler, Gregor, Zimmerer, David, Denner, Stefan, Baumgartner, Michael, Isensee, Fabian, Jaini, Priyank, Maier-Hein, Klaus H.
What representation do deep neural networks learn? How similar are images to each other for neural networks? Despite the overwhelming success of deep learning methods key questions about their internal workings still remain largely unanswered, due to
Externí odkaz:
http://arxiv.org/abs/2410.23107
Autor:
Denner, Stefan, Bujotzek, Markus, Bounias, Dimitrios, Zimmerer, David, Stock, Raphael, Jäger, Paul F., Maier-Hein, Klaus
Medical image classification in radiology faces significant challenges, particularly in generalizing to unseen pathologies. In contrast, CLIP offers a promising solution by leveraging multimodal learning to improve zero-shot classification performanc
Externí odkaz:
http://arxiv.org/abs/2408.15802
Autor:
Zenk, Maximilian, Zimmerer, David, Isensee, Fabian, Traub, Jeremias, Norajitra, Tobias, Jäger, Paul F., Maier-Hein, Klaus
Semantic segmentation is an essential component of medical image analysis research, with recent deep learning algorithms offering out-of-the-box applicability across diverse datasets. Despite these advancements, segmentation failures remain a signifi
Externí odkaz:
http://arxiv.org/abs/2406.03323
Autor:
Denner, Stefan, Zimmerer, David, Bounias, Dimitrios, Bujotzek, Markus, Xiao, Shuhan, Kausch, Lisa, Schader, Philipp, Penzkofer, Tobias, Jäger, Paul F., Maier-Hein, Klaus
Content-based image retrieval (CBIR) has the potential to significantly improve diagnostic aid and medical research in radiology. Current CBIR systems face limitations due to their specialization to certain pathologies, limiting their utility. In res
Externí odkaz:
http://arxiv.org/abs/2403.06567
Autor:
Mroczkowski, Tony, Gallardo, Patricio A., Timpe, Martin, Kiselev, Aleksej, Groh, Manuel, Kaercher, Hans, Reichert, Matthias, Cicone, Claudia, Puddu, Roberto, Dubois-dit-Bonclaude, Pierre, Bok, Daniel, Dahl, Erik, Macintosh, Mike, Dicker, Simon, Viole, Isabelle, Sartori, Sabrina, Venegas, Guillermo Andrés Valenzuela, Zeyringer, Marianne, Niemack, Michael, Poppi, Sergio, Olguin, Rodrigo, Hatziminaoglou, Evanthia, De Breuck, Carlos, Klaassen, Pamela, Montenegro-Montes, Francisco Miguel, Zimmerer, Thomas
The submillimeter and millimeter ((sub-)mm) sky contains a vast wealth of information that is both complementary and inaccessible to other wavelengths. Over half the light we receive is observable at (sub-)mm wavelengths, yet we have mapped only a sm
Externí odkaz:
http://arxiv.org/abs/2402.18645
Autor:
Koehler, Gregor, Wald, Tassilo, Ulrich, Constantin, Zimmerer, David, Jaeger, Paul F., Franke, Jörg K. H., Kohl, Simon, Isensee, Fabian, Maier-Hein, Klaus H.
Despite the remarkable success of deep learning systems over the last decade, a key difference still remains between neural network and human decision-making: As humans, we cannot only form a decision on the spot, but also ponder, revisiting an initi
Externí odkaz:
http://arxiv.org/abs/2309.07513
Autor:
Mroczkowski, Tony, Cicone, Claudia, Reichert, Matthias, Gallardo, Patricio, Kaercher, Hans, Hills, Richard, Bok, Daniel, Dahl, Erik, Dubois-dit-Bonclaude, Pierre, Kiselev, Aleksej, Timpe, Martin, Zimmerer, Thomas, Dicker, Simon, Macintosh, Mike, Klaassen, Pamela, Niemack, Michael
Publikováno v:
2023 XXXVth General Assembly and Scientific Symposium of the International Union of Radio Science (URSI GASS), Sapporo, Japan, 2023
The Atacama Large Aperture Submillimeter Telescope (AtLAST) aims to be the premier next generation large diameter (50 meter) single dish observatory capable of observations across the millimeter/submillimeter spectrum, from 30~GHz to 1~THz. AtLAST wi
Externí odkaz:
http://arxiv.org/abs/2308.10952
Autor:
Wald, Tassilo, Ulrich, Constantin, Isensee, Fabian, Zimmerer, David, Koehler, Gregor, Baumgartner, Michael, Maier-Hein, Klaus H.
Independently trained machine learning models tend to learn similar features. Given an ensemble of independently trained models, this results in correlated predictions and common failure modes. Previous attempts focusing on decorrelation of output pr
Externí odkaz:
http://arxiv.org/abs/2307.02516
Autor:
Roy, Saikat, Wald, Tassilo, Koehler, Gregor, Rokuss, Maximilian R., Disch, Nico, Holzschuh, Julius, Zimmerer, David, Maier-Hein, Klaus H.
Foundation models have taken over natural language processing and image generation domains due to the flexibility of prompting. With the recent introduction of the Segment Anything Model (SAM), this prompt-driven paradigm has entered image segmentati
Externí odkaz:
http://arxiv.org/abs/2304.05396
Autor:
Eisenmann, Matthias, Reinke, Annika, Weru, Vivienn, Tizabi, Minu Dietlinde, Isensee, Fabian, Adler, Tim J., Ali, Sharib, Andrearczyk, Vincent, Aubreville, Marc, Baid, Ujjwal, Bakas, Spyridon, Balu, Niranjan, Bano, Sophia, Bernal, Jorge, Bodenstedt, Sebastian, Casella, Alessandro, Cheplygina, Veronika, Daum, Marie, de Bruijne, Marleen, Depeursinge, Adrien, Dorent, Reuben, Egger, Jan, Ellis, David G., Engelhardt, Sandy, Ganz, Melanie, Ghatwary, Noha, Girard, Gabriel, Godau, Patrick, Gupta, Anubha, Hansen, Lasse, Harada, Kanako, Heinrich, Mattias, Heller, Nicholas, Hering, Alessa, Huaulmé, Arnaud, Jannin, Pierre, Kavur, Ali Emre, Kodym, Oldřich, Kozubek, Michal, Li, Jianning, Li, Hongwei, Ma, Jun, Martín-Isla, Carlos, Menze, Bjoern, Noble, Alison, Oreiller, Valentin, Padoy, Nicolas, Pati, Sarthak, Payette, Kelly, Rädsch, Tim, Rafael-Patiño, Jonathan, Bawa, Vivek Singh, Speidel, Stefanie, Sudre, Carole H., van Wijnen, Kimberlin, Wagner, Martin, Wei, Donglai, Yamlahi, Amine, Yap, Moi Hoon, Yuan, Chun, Zenk, Maximilian, Zia, Aneeq, Zimmerer, David, Aydogan, Dogu Baran, Bhattarai, Binod, Bloch, Louise, Brüngel, Raphael, Cho, Jihoon, Choi, Chanyeol, Dou, Qi, Ezhov, Ivan, Friedrich, Christoph M., Fuller, Clifton, Gaire, Rebati Raman, Galdran, Adrian, Faura, Álvaro García, Grammatikopoulou, Maria, Hong, SeulGi, Jahanifar, Mostafa, Jang, Ikbeom, Kadkhodamohammadi, Abdolrahim, Kang, Inha, Kofler, Florian, Kondo, Satoshi, Kuijf, Hugo, Li, Mingxing, Luu, Minh Huan, Martinčič, Tomaž, Morais, Pedro, Naser, Mohamed A., Oliveira, Bruno, Owen, David, Pang, Subeen, Park, Jinah, Park, Sung-Hong, Płotka, Szymon, Puybareau, Elodie, Rajpoot, Nasir, Ryu, Kanghyun, Saeed, Numan, Shephard, Adam, Shi, Pengcheng, Štepec, Dejan, Subedi, Ronast, Tochon, Guillaume, Torres, Helena R., Urien, Helene, Vilaça, João L., Wahid, Kareem Abdul, Wang, Haojie, Wang, Jiacheng, Wang, Liansheng, Wang, Xiyue, Wiestler, Benedikt, Wodzinski, Marek, Xia, Fangfang, Xie, Juanying, Xiong, Zhiwei, Yang, Sen, Yang, Yanwu, Zhao, Zixuan, Maier-Hein, Klaus, Jäger, Paul F., Kopp-Schneider, Annette, Maier-Hein, Lena
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really
Externí odkaz:
http://arxiv.org/abs/2303.17719