Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Behrendt Finn"'
Publikováno v:
Current Directions in Biomedical Engineering, Vol 8, Iss 1, Pp 34-37 (2022)
Radiographs are a versatile diagnostic tool for the detection and assessment of pathologies, for treatment planning or for navigation and localization purposes in clinical interventions. However, their interpretation and assessment by radiologists ca
Externí odkaz:
https://doaj.org/article/1269f9eefc134d21bf9b0982ec13430c
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:
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
Publikováno v:
Current Directions in Biomedical Engineering, Vol 6, Iss 1, Pp 790-5 (2020)
Robot-assisted minimally-invasive surgery is increasingly used in clinical practice. Force feedback offers potential to develop haptic feedback for surgery systems. Forces can be estimated in a vision-based way by capturing deformation observed in 2D
Externí odkaz:
https://doaj.org/article/76107b31d57b4da09542454c3aaa8161
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:
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:
Bhattacharya, Debayan, Latus, Sarah, Behrendt, Finn, Thimm, Florin, Eggert, Dennis, Betz, Christian, Schlaefer, Alexander
Needle positioning is essential for various medical applications such as epidural anaesthesia. Physicians rely on their instincts while navigating the needle in epidural spaces. Thereby, identifying the tissue structures may be helpful to the physici
Externí odkaz:
http://arxiv.org/abs/2304.13574
Autor:
Bhattacharya, Debayan, Behrendt, Finn, Becker, Benjamin Tobias, Beyersdorff, Dirk, Petersen, Elina, Petersen, Marvin, Cheng, Bastian, Eggert, Dennis, Betz, Christian, Hoffmann, Anna Sophie, Schlaefer, Alexander
Paranasal anomalies are commonly discovered during routine radiological screenings and can present with a wide range of morphological features. This diversity can make it difficult for convolutional neural networks (CNNs) to accurately classify these
Externí odkaz:
http://arxiv.org/abs/2303.17915