Results 311 to 320 of about 7,074,171 (358)

TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021
Medical image segmentation - the prerequisite of numerous clinical needs - has been significantly prospered by recent advances in convolutional neural networks (CNNs). However, it exhibits general limitations on modeling explicit long-range relation, and
Yundong Zhang, Huiye Liu, Qiang Hu
semanticscholar   +1 more source

Segmentation of medical images

Image and Vision Computing, 1993
Abstract Segmentation and labelling remains the weakest step in many medical vision applications. This paper illustrates an approach based on generic modules which are designed to solve typical problems encountered in various applications, and which are controllable through adaptation of their parameters.
Rudi, Deklerck, Jan, Cornelis, Bister, Michel   +2 more
openaire   +2 more sources

Image segmentation techniques

Computer Vision, Graphics, and Image Processing, 1984
There are now a wide Abstract There are now a wide variety of image segmentation techniques, some considered general purpose and some designed for specific classes of images. These techniques can be classified as: measurement space guided spatial clustering, single linkage region growing schemes, hybrid linkage region growing schemes, centroid linkage ...
Robert M. Haralick, Linda G. Shapiro
openaire   +1 more source

U-Net: Convolutional Networks for Biomedical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015
There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available ...
O. Ronneberger, P. Fischer, T. Brox
semanticscholar   +1 more source

Scaling Open-Vocabulary Image Segmentation with Image-Level Labels

European Conference on Computer Vision, 2021
We design an open-vocabulary image segmentation model to organize an image into meaningful regions indicated by arbitrary texts. Recent works (CLIP and ALIGN), despite attaining impressive open-vocabulary classification accuracy with image-level caption ...
Golnaz Ghiasi, Xiuye Gu, Yin Cui, Tsung-Yi Lin   +3 more
semanticscholar   +1 more source

UniverSeg: Universal Medical Image Segmentation

IEEE International Conference on Computer Vision, 2023
While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task,
V. Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, M. Sabuncu, J. Guttag, Adrian V. Dalca   +5 more
semanticscholar   +1 more source

From CNN to Transformer: A Review of Medical Image Segmentation Models

Journal of Imaging Informatics in Medicine, 2023
Medical image segmentation is an important step in medical image analysis, especially as a crucial prerequisite for efficient disease diagnosis and treatment. The use of deep learning for image segmentation has become a prevalent trend.
Wenjian Yao, Jiajun Bai, Wei Liao, Yuheng Chen, Mengjuan Liu, Yao Xie   +5 more
semanticscholar   +1 more source

nnFormer: Volumetric Medical Image Segmentation via a 3D Transformer

IEEE Transactions on Image Processing, 2021
Transformer, the model of choice for natural language processing, has drawn scant attention from the medical imaging community. Given the ability to exploit long-term dependencies, transformers are promising to help atypical convolutional neural networks
Hong-Yu Zhou, J. Guo, Yinghao Zhang, Lequan Yu, Liansheng Wang, Yizhou Yu   +5 more
semanticscholar   +1 more source

UTNet: A Hybrid Transformer Architecture for Medical Image Segmentation

International Conference on Medical Image Computing and Computer-Assisted Intervention, 2021
Transformer architecture has emerged to be successful in a number of natural language processing tasks. However, its applications to medical vision remain largely unexplored.
Yunhe Gao, Mu Zhou, Dimitris N. Metaxas
semanticscholar   +1 more source

U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation

arXiv.org
Convolutional Neural Networks (CNNs) and Transformers have been the most popular architectures for biomedical image segmentation, but both of them have limited ability to handle long-range dependencies because of inherent locality or computational ...
Jun Ma, Feifei Li, Bo Wang
semanticscholar   +1 more source