Zobrazeno 1 - 10
of 1 003
pro vyhledávání: '"Vercauteren, Tom"'
Autor:
Alabi, Oluwatosin, Toe, Ko Ko Zayar, Zhou, Zijian, Budd, Charlie, Raison, Nicholas, Shi, Miaojing, Vercauteren, Tom
In laparoscopic and robotic surgery, precise tool instance segmentation is an essential technology for advanced computer-assisted interventions. Although publicly available procedures of routine surgeries exist, they often lack comprehensive annotati
Externí odkaz:
http://arxiv.org/abs/2406.16039
Autor:
LaBella, Dominic, Schumacher, Katherine, Mix, Michael, Leu, Kevin, McBurney-Lin, Shan, Nedelec, Pierre, Villanueva-Meyer, Javier, Shapey, Jonathan, Vercauteren, Tom, Chia, Kazumi, Al-Salihi, Omar, Leu, Justin, Halasz, Lia, Velichko, Yury, Wang, Chunhao, Kirkpatrick, John, Floyd, Scott, Reitman, Zachary J., Mullikin, Trey, Bagci, Ulas, Sachdev, Sean, Hattangadi-Gluth, Jona A., Seibert, Tyler, Farid, Nikdokht, Puett, Connor, Pease, Matthew W., Shiue, Kevin, Anwar, Syed Muhammad, Faghani, Shahriar, Haider, Muhammad Ammar, Warman, Pranav, Albrecht, Jake, Jakab, András, Moassefi, Mana, Chung, Verena, Aristizabal, Alejandro, Karargyris, Alexandros, Kassem, Hasan, Pati, Sarthak, Sheller, Micah, Huang, Christina, Coley, Aaron, Ghanta, Siddharth, Schneider, Alex, Sharp, Conrad, Saluja, Rachit, Kofler, Florian, Lohmann, Philipp, Vollmuth, Phillipp, Gagnon, Louis, Adewole, Maruf, Li, Hongwei Bran, Kazerooni, Anahita Fathi, Tahon, Nourel Hoda, Anazodo, Udunna, Moawad, Ahmed W., Menze, Bjoern, Linguraru, Marius George, Aboian, Mariam, Wiestler, Benedikt, Baid, Ujjwal, Conte, Gian-Marco, Rauschecker, Andreas M. T., Nada, Ayman, Abayazeed, Aly H., Huang, Raymond, de Verdier, Maria Correia, Rudie, Jeffrey D., Bakas, Spyridon, Calabrese, Evan
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aims to advance automated segmentation algorithms using the largest known multi-institutional dataset of radiotherapy planning brain MRIs with expert-annotated target
Externí odkaz:
http://arxiv.org/abs/2405.18383
Shear wave elastography involves applying a non-invasive acoustic radiation force to the tissue and imaging the induced deformation to infer its mechanical properties. This work investigates the use of convolutional neural networks to improve displac
Externí odkaz:
http://arxiv.org/abs/2404.16953
Whole brain parcellation requires inferring hundreds of segmentation labels in large image volumes and thus presents significant practical challenges for deep learning approaches. We introduce label merge-and-split, a method that first greatly reduce
Externí odkaz:
http://arxiv.org/abs/2404.10572
Deep neural networks for medical image segmentation often produce overconfident results misaligned with empirical observations. Such miscalibration, challenges their clinical translation. We propose to use marginal L1 average calibration error (mL1-A
Externí odkaz:
http://arxiv.org/abs/2403.06759
Radiology report generation (RRG) has attracted significant attention due to its potential to reduce the workload of radiologists. Current RRG approaches are still unsatisfactory against clinical standards. This paper introduces a novel RRG method, \
Externí odkaz:
http://arxiv.org/abs/2403.06728
Autor:
Budd, Charlie, Vercauteren, Tom
Relative monocular depth, inferring depth up to shift and scale from a single image, is an active research topic. Recent deep learning models, trained on large and varied meta-datasets, now provide excellent performance in the domain of natural image
Externí odkaz:
http://arxiv.org/abs/2403.06683
Autor:
Bahl, Anisha, Segaud, Silvere, Xie, Yijing, Shapey, Jonathan, Bergholt, Mads, Vercauteren, Tom
Information about tissue oxygen saturation ($StO_2$) and other related important physiological parameters can be extracted from diffuse reflectance spectra measured through non-contact imaging. Three analytical optical reflectance models for homogene
Externí odkaz:
http://arxiv.org/abs/2312.12935
Autor:
Huber, Martin, Mower, Christopher E., Ourselin, Sebastien, Vercauteren, Tom, Bergeles, Christos
The LBR-Stack is a collection of packages that simplify the usage and extend the capabilities of KUKA's Fast Robot Interface (FRI). It is designed for mission critical hard real-time applications. Supported are the KUKA LBR Med 7/14 and KUKA LBR IIWA
Externí odkaz:
http://arxiv.org/abs/2311.12709
Autor:
Fernandez, Virginia, Pinaya, Walter Hugo Lopez, Borges, Pedro, Graham, Mark S., Vercauteren, Tom, Cardoso, M. Jorge
Generative modelling and synthetic data can be a surrogate for real medical imaging datasets, whose scarcity and difficulty to share can be a nuisance when delivering accurate deep learning models for healthcare applications. In recent years, there h
Externí odkaz:
http://arxiv.org/abs/2311.04552