Zobrazeno 1 - 10
of 120
pro vyhledávání: '"Skibbe, Henrik"'
We introduce "PatchMorph," an new stochastic deep learning algorithm tailored for unsupervised 3D brain image registration. Unlike other methods, our method uses compact patches of a constant small size to derive solutions that can combine global tra
Externí odkaz:
http://arxiv.org/abs/2312.06958
We propose a novel image registration method based on implicit neural representations that addresses the challenging problem of registering a pair of brain images with similar anatomical structures, but where one image contains additional features or
Externí odkaz:
http://arxiv.org/abs/2308.04039
In this work, we investigate the usefulness of vision-language models (VLMs) and large language models for binary few-shot classification of medical images. We utilize the GPT-4 model to generate text descriptors that encapsulate the shape and textur
Externí odkaz:
http://arxiv.org/abs/2308.04005
In this paper, we propose a novel two-component loss for biomedical image segmentation tasks called the Instance-wise and Center-of-Instance (ICI) loss, a loss function that addresses the instance imbalance problem commonly encountered when using pix
Externí odkaz:
http://arxiv.org/abs/2304.06229
Autor:
Poon, Charissa, Rachmadi, Muhammad Febrian, Byra, Michal, Schlachter, Matthias, Xu, Binbin, Shimogori, Tomomi, Skibbe, Henrik
We present the first automated pipeline to create an atlas of in situ hybridization gene expression in the adult marmoset brain in the same stereotaxic space. The pipeline consists of segmentation of gene expression from microscopy images and registr
Externí odkaz:
http://arxiv.org/abs/2303.06857
Convolutional neural networks are the way to solve arbitrary image segmentation tasks. However, when images are large, memory demands often exceed the available resources, in particular on a common GPU. Especially in biomedical imaging, where 3D imag
Externí odkaz:
http://arxiv.org/abs/2206.03210
Publikováno v:
In iScience 18 October 2024 27(10)
Publikováno v:
In Computers in Biology and Medicine May 2024 174
Autor:
Gutierrez, Carlos Enrique, Skibbe, Henrik, Nakae, Ken, Tsukada, Hiromichi, Lienard, Jean, Watakabe, Akiya, Hata, Junichi, Reisert, Marco, Woodward, Alexander, Okano, Hideyuki, Yamamori, Tetsuo, Yamaguchi, Yoko, Ishii, Shin, Doya, Kenji
Diffusion-weighted magnetic resonance imaging (dMRI) allows non-invasive investigation of whole-brain connectivity, which can potentially help to reveal the brain's global network architecture and abnormalities involved in neurological and mental dis
Externí odkaz:
http://arxiv.org/abs/1911.13215
Autor:
Shima, Yasuyuki, Skibbe, Henrik, Sasagawa, Yohei, Fujimori, Noriko, Iwayama, Yoshimi, Isomura-Matoba, Ayako, Yano, Minoru, Ichikawa, Takumi, Nikaido, Itoshi, Hattori, Nobutaka, Kato, Tadafumi
Publikováno v:
In Cell Reports 31 October 2023 42(10)