Zobrazeno 1 - 10
of 3 550
pro vyhledávání: '"Böcking, A"'
The selection of algorithms is a crucial step in designing AI services for real-world time series classification use cases. Traditional methods such as neural architecture search, automated machine learning, combined algorithm selection, and hyperpar
Externí odkaz:
http://arxiv.org/abs/2409.08636
Autor:
Cartucho, Joao, Weld, Alistair, Tukra, Samyakh, Xu, Haozheng, Matsuzaki, Hiroki, Ishikawa, Taiyo, Kwon, Minjun, Jang, Yong Eun, Kim, Kwang-Ju, Lee, Gwang, Bai, Bizhe, Kahrs, Lueder, Boecking, Lars, Allmendinger, Simeon, Muller, Leopold, Zhang, Yitong, Jin, Yueming, Bano, Sophia, Vasconcelos, Francisco, Reiter, Wolfgang, Hajek, Jonas, Silva, Bruno, Lima, Estevao, Vilaca, Joao L., Queiros, Sandro, Giannarou, Stamatia
This paper introduces the ``SurgT: Surgical Tracking" challenge which was organised in conjunction with MICCAI 2022. There were two purposes for the creation of this challenge: (1) the establishment of the first standardised benchmark for the researc
Externí odkaz:
http://arxiv.org/abs/2302.03022
Autor:
Bannur, Shruthi, Hyland, Stephanie, Liu, Qianchu, Pérez-García, Fernando, Ilse, Maximilian, Castro, Daniel C., Boecking, Benedikt, Sharma, Harshita, Bouzid, Kenza, Thieme, Anja, Schwaighofer, Anton, Wetscherek, Maria, Lungren, Matthew P., Nori, Aditya, Alvarez-Valle, Javier, Oktay, Ozan
Self-supervised learning in vision-language processing exploits semantic alignment between imaging and text modalities. Prior work in biomedical VLP has mostly relied on the alignment of single image and report pairs even though clinical notes common
Externí odkaz:
http://arxiv.org/abs/2301.04558
Autor:
Boecking, Benedikt, Usuyama, Naoto, Bannur, Shruthi, Castro, Daniel C., Schwaighofer, Anton, Hyland, Stephanie, Wetscherek, Maria, Naumann, Tristan, Nori, Aditya, Alvarez-Valle, Javier, Poon, Hoifung, Oktay, Ozan
Publikováno v:
Computer Vision - ECCV 2022, LNCS vol 13696, pp 1-21
Multi-modal data abounds in biomedicine, such as radiology images and reports. Interpreting this data at scale is essential for improving clinical care and accelerating clinical research. Biomedical text with its complex semantics poses additional ch
Externí odkaz:
http://arxiv.org/abs/2204.09817
Clustering under pairwise constraints is an important knowledge discovery tool that enables the learning of appropriate kernels or distance metrics to improve clustering performance. These pairwise constraints, which come in the form of must-link and
Externí odkaz:
http://arxiv.org/abs/2203.12546
Autor:
Boecking, Benedikt, Roberts, Nicholas, Neiswanger, Willie, Ermon, Stefano, Sala, Frederic, Dubrawski, Artur
Many promising applications of supervised machine learning face hurdles in the acquisition of labeled data in sufficient quantity and quality, creating an expensive bottleneck. To overcome such limitations, techniques that do not depend on ground tru
Externí odkaz:
http://arxiv.org/abs/2203.12023
In order to commemorate Alfred Land\'e's unriddling of the anomalous Zeeman Effect a century ago, we reconstruct his seminal contribution to atomic physics in light of the atomic models available at the time. Land\'e recognized that the coupling of q
Externí odkaz:
http://arxiv.org/abs/2203.07833
Analysing electrocardiograms (ECGs) is an inexpensive and non-invasive, yet powerful way to diagnose heart disease. ECG studies using Machine Learning to automatically detect abnormal heartbeats so far depend on large, manually annotated datasets. Wh
Externí odkaz:
http://arxiv.org/abs/2201.02936
Autor:
Filippova, Olga T., Boecking, Katherine, Broach, Vance, Gardner, Ginger J., Sonoda, Yukio, Chi, Dennis S., Zivanovic, Oliver, Long Roche, Kara
Publikováno v:
In Gynecologic Oncology August 2024 187:80-84
