Zobrazeno 1 - 10
of 1 531
pro vyhledávání: '"Schlaefer, A."'
Autor:
Behrendt, Finn, Bhattacharya, Debayan, Mieling, Robin, Maack, Lennart, Krüger, Julia, Opfer, Roland, Schlaefer, Alexander
Unsupervised Anomaly Detection (UAD) methods rely on healthy data distributions to identify anomalies as outliers. In brain MRI, a common approach is reconstruction-based UAD, where generative models reconstruct healthy brain MRIs, and anomalies are
Externí odkaz:
http://arxiv.org/abs/2407.12474
Autor:
Grube, Sarah, Neidhardt, Maximilian, Herrmann, Anna-Katarina, Sprenger, Johanna, Abdolazizi, Kian, Latus, Sarah, Cyron, Christian J., Schlaefer, Alexander
Soft tissue elasticity is directly related to different stages of diseases and can be used for tissue identification during minimally invasive procedures. By palpating a tissue with a robot in a minimally invasive fashion force-displacement curves ca
Externí odkaz:
http://arxiv.org/abs/2406.09947
Autor:
Bhattacharya, Debayan, Behrendt, Finn, Becker, Benjamin Tobias, Maack, Lennart, Beyersdorff, Dirk, Petersen, Elina, Petersen, Marvin, Cheng, Bastian, Eggert, Dennis, Betz, Christian, Hoffmann, Anna Sophie, Schlaefer, Alexander
Purpose: Paranasal anomalies, frequently identified in routine radiological screenings, exhibit diverse morphological characteristics. Due to the diversity of anomalies, supervised learning methods require large labelled dataset exhibiting diverse an
Externí odkaz:
http://arxiv.org/abs/2404.18599
Sliced Online Model Checking for Optimizing the Beam Scheduling Problem in Robotic Radiation Therapy
Publikováno v:
EPTCS 399, 2024, pp. 193-209
In robotic radiation therapy, high-energy photon beams from different directions are directed at a target within the patient. Target motion can be tracked by robotic ultrasound and then compensated by synchronous beam motion. However, moving the beam
Externí odkaz:
http://arxiv.org/abs/2403.18918
Autor:
Behrendt, Finn, Bhattacharya, Debayan, Maack, Lennart, Krüger, Julia, Opfer, Roland, Mieling, Robin, Schlaefer, Alexander
Supervised deep learning techniques show promise in medical image analysis. However, they require comprehensive annotated data sets, which poses challenges, particularly for rare diseases. Consequently, unsupervised anomaly detection (UAD) emerges as
Externí odkaz:
http://arxiv.org/abs/2403.14262
Autor:
Neidhardt, Maximilian, Mieling, Robin, Latus, Sarah, Fischer, Martin, Maurer, Tobias, Schlaefer, Alexander
Robot-assisted surgery has advantages compared to conventional laparoscopic procedures, e.g., precise movement of the surgical instruments, improved dexterity, and high-resolution visualization of the surgical field. However, mechanical tissue proper
Externí odkaz:
http://arxiv.org/abs/2403.09256
Autor:
Bhattacharya, Debayan, Reuter, Konrad, Behrendt, Finn, Maack, Lennart, Grube, Sarah, Schlaefer, Alexander
Commonly employed in polyp segmentation, single image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leverages vid
Externí odkaz:
http://arxiv.org/abs/2402.11585
Autor:
Sogancioglu, Ecem, van Ginneken, Bram, Behrendt, Finn, Bengs, Marcel, Schlaefer, Alexander, Radu, Miron, Xu, Di, Sheng, Ke, Scalzo, Fabien, Marcus, Eric, Papa, Samuele, Teuwen, Jonas, Scholten, Ernst Th., Schalekamp, Steven, Hendrix, Nils, Jacobs, Colin, Hendrix, Ward, Sánchez, Clara I, Murphy, Keelin
Pulmonary nodules may be an early manifestation of lung cancer, the leading cause of cancer-related deaths among both men and women. Numerous studies have established that deep learning methods can yield high-performance levels in the detection of lu
Externí odkaz:
http://arxiv.org/abs/2401.02192
Autor:
Behrendt, Finn, Bhattacharya, Debayan, Mieling, Robin, Maack, Lennart, Krüger, Julia, Opfer, Roland, Schlaefer, Alexander
Unsupervised anomaly detection in Brain MRIs aims to identify abnormalities as outliers from a healthy training distribution. Reconstruction-based approaches that use generative models to learn to reconstruct healthy brain anatomy are commonly used f
Externí odkaz:
http://arxiv.org/abs/2312.04215
Autor:
Neidhardt, M., Schmidt, S. Gerlach F. N., Fiedler, I. A. K., Grube, S., Busse, B., Schlaefer, A.
Training helps to maintain and improve sufficient muscle function, body control, and body coordination. These are important to reduce the risk of fracture incidents caused by falls, especially for the elderly or people recovering from injury. Virtual
Externí odkaz:
http://arxiv.org/abs/2308.03375