Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Lenga, Matthias"'
Publikováno v:
In: Mukhopadhyay, A., Oksuz, I., Engelhardt, S., Mehrof, D., Yuan, Y. (eds) Deep Generative Models. DGM4MICCAI 2024. Lecture Notes in Computer Science, vol 15224. Springer, Cham
Reference metrics have been developed to objectively and quantitatively compare two images. Especially for evaluating the quality of reconstructed or compressed images, these metrics have shown very useful. Extensive tests of such metrics on benchmar
Externí odkaz:
http://arxiv.org/abs/2408.06075
While content-based image retrieval (CBIR) has been extensively studied in natural image retrieval, its application to medical images presents ongoing challenges, primarily due to the 3D nature of medical images. Recent studies have shown the potenti
Externí odkaz:
http://arxiv.org/abs/2405.09334
Image-to-image translation can create large impact in medical imaging, as images can be synthetically transformed to other modalities, sequence types, higher resolutions or lower noise levels. To ensure patient safety, these methods should be validat
Externí odkaz:
http://arxiv.org/abs/2405.08431
This work addresses the Brain Magnetic Resonance Image Synthesis for Tumor Segmentation (BraSyn) challenge, which was hosted as part of the Brain Tumor Segmentation (BraTS) challenge in 2023. In this challenge, researchers are invited to synthesize a
Externí odkaz:
http://arxiv.org/abs/2403.07800
Near- and duplicate image detection is a critical concern in the field of medical imaging. Medical datasets often contain similar or duplicate images from various sources, which can lead to significant performance issues and evaluation biases, especi
Externí odkaz:
http://arxiv.org/abs/2312.07273
A wide range of imaging techniques and data formats available for medical images make accurate retrieval from image databases challenging. Efficient retrieval systems are crucial in advancing medical research, enabling large-scale studies and innovat
Externí odkaz:
http://arxiv.org/abs/2311.13547
In recent years, deep learning has been applied to a wide range of medical imaging and image processing tasks. In this work, we focus on the estimation of epistemic uncertainty for 3D medical image-to-image translation. We propose a novel model uncer
Externí odkaz:
http://arxiv.org/abs/2311.12153
Generative adversarial networks (GANs) have shown remarkable success in generating realistic images and are increasingly used in medical imaging for image-to-image translation tasks. However, GANs tend to suffer from a frequency bias towards low freq
Externí odkaz:
http://arxiv.org/abs/2303.15938
Autor:
Truong, Tuan1 (AUTHOR) matthias.lenga@bayer.com, Lenga, Matthias1 (AUTHOR) sadegh.mohammadi@bayer.com, Serrurier, Antoine2 (AUTHOR) aserrurier@ukaachen.de, Mohammadi, Sadegh1 (AUTHOR)
Publikováno v:
Sensors (14248220). Oct2024, Vol. 24 Issue 19, p6176. 21p.
It is common practice to reuse models initially trained on different data to increase downstream task performance. Especially in the computer vision domain, ImageNet-pretrained weights have been successfully used for various tasks. In this work, we i
Externí odkaz:
http://arxiv.org/abs/2207.14508