Zobrazeno 1 - 10
of 288
pro vyhledávání: '"Peeken, Jan"'
Autor:
Shahzadi, Iram, Zwanenburg, Alex, Lattermann, Annika, Linge, Annett, Baldus, Christian, Peeken, Jan C., Combs, Stephanie E., Diefenhardt, Markus, Rödel, Claus, Kirste, Simon, Grosu, Anca-Ligia, Baumann, Michael, Krause, Mechthild, Troost, Esther G. C., Löck, Steffen
Radiomics analyses commonly apply imaging features of different complexity for the prediction of the endpoint of interest. However, the prognostic value of each feature class is generally unclear. Furthermore, many radiomics models lack independent e
Externí odkaz:
https://tud.qucosa.de/id/qucosa%3A90623
https://tud.qucosa.de/api/qucosa%3A90623/attachment/ATT-0/
https://tud.qucosa.de/api/qucosa%3A90623/attachment/ATT-0/
Autor:
Kiechle, Johannes, Lang, Daniel M., Fischer, Stefan M., Felsner, Lina, Peeken, Jan C., Schnabel, Julia A.
Recent studies have underscored the capabilities of natural imaging foundation models to serve as powerful feature extractors, even in a zero-shot setting for medical imaging data. Most commonly, a shallow multi-layer perceptron (MLP) is appended to
Externí odkaz:
http://arxiv.org/abs/2407.17219
Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks
Autor:
Fischer, Stefan M., Felsner, Lina, Osuala, Richard, Kiechle, Johannes, Lang, Daniel M., Peeken, Jan C., Schnabel, Julia A.
In this work, we introduce Progressive Growing of Patch Size, a resource-efficient implicit curriculum learning approach for dense prediction tasks. Our curriculum approach is defined by growing the patch size during model training, which gradually i
Externí odkaz:
http://arxiv.org/abs/2407.07853
Publikováno v:
Machine.Learning.for.Biomedical.Imaging. 2 (2024)
Pathological lymph node delineation is crucial in cancer diagnosis, progression assessment, and treatment planning. The MICCAI 2023 Lymph Node Quantification Challenge published the first public dataset for pathological lymph node segmentation in the
Externí odkaz:
http://arxiv.org/abs/2406.14365
Autor:
Hartong, Nanna E., Sachpazidis, Ilias, Blanck, Oliver, Etzel, Lucas, Peeken, Jan C., Combs, Stephanie E., Urbach, Horst, Zaitsev, Maxim, Baltas, Dimos, Popp, Ilinca, Grosu, Anca-Ligia, Fechter, Tobias
Background: The aim of this study was to investigate the role of clinical, dosimetric and pretherapeutic magnetic resonance imaging (MRI) features for lesion-specific outcome prediction of stereotactic radiotherapy (SRT) in patients with brain metast
Externí odkaz:
http://arxiv.org/abs/2405.20825
Autor:
Erdur, Ayhan Can, Scholz, Daniel, Buchner, Josef A., Combs, Stephanie E., Rueckert, Daniel, Peeken, Jan C.
Brain metastases (BMs) are the most frequently occurring brain tumors. The treatment of patients having multiple BMs with stereo tactic radiosurgery necessitates accurate localization of the metastases. Neural networks can assist in this time-consumi
Externí odkaz:
http://arxiv.org/abs/2310.02829
Autor:
Ziller, Alexander, Erdur, Ayhan Can, Trigui, Marwa, Güvenir, Alp, Mueller, Tamara T., Müller, Philip, Jungmann, Friederike, Brandt, Johannes, Peeken, Jan, Braren, Rickmer, Rueckert, Daniel, Kaissis, Georgios
Training Artificial Intelligence (AI) models on 3D images presents unique challenges compared to the 2D case: Firstly, the demand for computational resources is significantly higher, and secondly, the availability of large datasets for pre-training i
Externí odkaz:
http://arxiv.org/abs/2307.06614
Autor:
Kofler, Florian, Shit, Suprosanna, Ezhov, Ivan, Fidon, Lucas, Horvath, Izabela, Al-Maskari, Rami, Li, Hongwei, Bhatia, Harsharan, Loehr, Timo, Piraud, Marie, Erturk, Ali, Kirschke, Jan, Peeken, Jan C., Vercauteren, Tom, Zimmer, Claus, Wiestler, Benedikt, Menze, Bjoern
Deep convolutional neural networks (CNN) have proven to be remarkably effective in semantic segmentation tasks. Most popular loss functions were introduced targeting improved volumetric scores, such as the Dice coefficient (DSC). By design, DSC can t
Externí odkaz:
http://arxiv.org/abs/2205.08209
Autor:
Navarro, Fernando, Sasahara, Guido, Shit, Suprosanna, Ezhov, Ivan, Peeken, Jan C., Combs, Stephanie E., Menze, Bjoern H.
Automatic localization and segmentation of organs-at-risk (OAR) in CT are essential pre-processing steps in medical image analysis tasks, such as radiation therapy planning. For instance, the segmentation of OAR surrounding tumors enables the maximiz
Externí odkaz:
http://arxiv.org/abs/2203.00624
Autor:
Peeken, Jan C., Etzel, Lucas, Tomov, Tim, Münch, Stefan, Schüttrumpf, Lars, Shaktour, Julius H., Kiechle, Johannes, Knebel, Carolin, Schaub, Stephanie K., Mayr, Nina A., Woodruff, Henry C., Lambin, Philippe, Gersing, Alexandra S., Bernhardt, Denise, Nyflot, Matthew J., Menze, Bjoern, Combs, Stephanie E., Navarro, Fernando
Publikováno v:
In Radiotherapy and Oncology August 2024 197