Zobrazeno 1 - 10
of 491
pro vyhledávání: '"Merhof, Dorit"'
Autor:
Reisenbüchler, Daniel, Luttner, Lucas, Schaadt, Nadine S., Feuerhake, Friedrich, Merhof, Dorit
In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techniques. Stain adaptation aims to reduce the generalization error betwee
Externí odkaz:
http://arxiv.org/abs/2406.19081
Autor:
Heidari, Moein, Kolahi, Sina Ghorbani, Karimijafarbigloo, Sanaz, Azad, Bobby, Bozorgpour, Afshin, Hatami, Soheila, Azad, Reza, Diba, Ali, Bagci, Ulas, Merhof, Dorit, Hacihaliloglu, Ilker
Sequence modeling plays a vital role across various domains, with recurrent neural networks being historically the predominant method of performing these tasks. However, the emergence of transformers has altered this paradigm due to their superior pe
Externí odkaz:
http://arxiv.org/abs/2406.03430
As a result of the rise of Transformer architectures in medical image analysis, specifically in the domain of medical image segmentation, a multitude of hybrid models have been created that merge the advantages of Convolutional Neural Networks (CNNs)
Externí odkaz:
http://arxiv.org/abs/2404.05102
Autor:
Heidari, Moein, Azad, Reza, Kolahi, Sina Ghorbani, Arimond, René, Niggemeier, Leon, Sulaiman, Alaa, Bozorgpour, Afshin, Aghdam, Ehsan Khodapanah, Kazerouni, Amirhossein, Hacihaliloglu, Ilker, Merhof, Dorit
Intrigued by the inherent ability of the human visual system to identify salient regions in complex scenes, attention mechanisms have been seamlessly integrated into various Computer Vision (CV) tasks. Building upon this paradigm, Vision Transformer
Externí odkaz:
http://arxiv.org/abs/2403.19882
Autor:
Kumari, Pratibha, Chauhan, Joohi, Bozorgpour, Afshin, Huang, Boqiang, Azad, Reza, Merhof, Dorit
Medical imaging analysis has witnessed remarkable advancements even surpassing human-level performance in recent years, driven by the rapid development of advanced deep-learning algorithms. However, when the inference dataset slightly differs from wh
Externí odkaz:
http://arxiv.org/abs/2312.17004
Autor:
Azad, Reza, Heidary, Moein, Yilmaz, Kadir, Hüttemann, Michael, Karimijafarbigloo, Sanaz, Wu, Yuli, Schmeink, Anke, Merhof, Dorit
Semantic image segmentation, the process of classifying each pixel in an image into a particular class, plays an important role in many visual understanding systems. As the predominant criterion for evaluating the performance of statistical models, l
Externí odkaz:
http://arxiv.org/abs/2312.05391
Current 3D semi-supervised segmentation methods face significant challenges such as limited consideration of contextual information and the inability to generate reliable pseudo-labels for effective unsupervised data use. To address these challenges,
Externí odkaz:
http://arxiv.org/abs/2311.12617
Accurate and automated segmentation of intervertebral discs (IVDs) in medical images is crucial for assessing spine-related disorders, such as osteoporosis, vertebral fractures, or IVD herniation. We present HCA-Net, a novel contextual attention netw
Externí odkaz:
http://arxiv.org/abs/2311.12486
Autor:
Kazerouni, Amirhossein, Karimijafarbigloo, Sanaz, Azad, Reza, Velichko, Yury, Bagci, Ulas, Merhof, Dorit
Semantic segmentation, a crucial task in computer vision, often relies on labor-intensive and costly annotated datasets for training. In response to this challenge, we introduce FuseNet, a dual-stream framework for self-supervised semantic segmentati
Externí odkaz:
http://arxiv.org/abs/2311.13069
Autor:
Schäfer, Raphael, Nicke, Till, Höfener, Henning, Lange, Annkristin, Merhof, Dorit, Feuerhake, Friedrich, Schulz, Volkmar, Lotz, Johannes, Kiessling, Fabian
Foundational models, pretrained on a large scale, have demonstrated substantial success across non-medical domains. However, training these models typically requires large, comprehensive datasets, which contrasts with the smaller and more heterogeneo
Externí odkaz:
http://arxiv.org/abs/2311.09847