Results 51 to 60 of about 29,416,981 (354)
MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation [PDF]
, 2007 This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem ...
Julio Villena-Román +7 more
core +2 more sources
Automated Disease Detection in Gastroscopy Videos Using Convolutional Neural Networks
Frontiers in Medicine, 2022 A large percentage of the world's population is affected by gastric diseases ranging from erosion and ulcer to serious ailments such as gastric cancer, which is mainly caused by Helicobacter pylori(H.pylori) infection.
Chenxi Zhang +6 more
doaj +1 more source
Distance Regularized Level Set Evolution for Medical Image Segmentation [PDF]
, 2013 Medical image is an important tool because it can be used for surgical planning and simulation, radiotherapy planning, and tracking the progress of disease. To analyze the medical image, it must partitioned into different segment using image segmentation
Pranowo, Pranowo, Rianto , Indra
core +1 more source
LViT: Language Meets Vision Transformer in Medical Image Segmentation [PDF]
IEEE Transactions on Medical Imaging, 2022 Deep learning has been widely used in medical image segmentation and other aspects. However, the performance of existing medical image segmentation models has been limited by the challenge of obtaining sufficient high-quality labeled data due to the ...
Zihan Li +8 more
semanticscholar +1 more source
Medical Image Processing on the GPU : Past, Present and Future [PDF]
, 2013 Graphics processing units (GPUs) are used today in a wide range of applications, mainly because they can dramatically accelerate parallel computing, are affordable and energy efficient.
Eklund, Anders, +7 more
core +1 more source
Evaluating Knowledge Transfer in the Neural Network for Medical Images
IEEE Access, 2023 The performance of deep learning models, such as convolutional neural networks (CNN)s, is highly dependent on the size of the training dataset. Consequently, it can be challenging to achieve satisfactory performance when training models from scratch in ...
S. Akbarian +3 more
doaj +1 more source
VoxelMorph: A Learning Framework for Deformable Medical Image Registration [PDF]
IEEE Transactions on Medical Imaging, 2018 We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich ...
Guha Balakrishnan +4 more
semanticscholar +1 more source
Medical Image Segmentation Review: The Success of U-Net [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized
Reza Azad +9 more
semanticscholar +1 more source
EMCAD: Efficient Multi-Scale Convolutional Attention Decoding for Medical Image Segmentation [PDF]
Computer Vision and Pattern RecognitionAn efficient and effective decoding mechanism is crucial in medical image segmentation, especially in scenarios with limited computational resources. However, these decoding mechanisms usually come with high computational costs.
M. Rahman, Mustafa Munir, R. Marculescu
semanticscholar +1 more source
UNETR++: Delving Into Efficient and Accurate 3D Medical Image Segmentation [PDF]
IEEE Transactions on Medical Imaging, 2022 Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-attention mechanism is one of the main building blocks that strives to capture long-range ...
Abdelrahman M. Shaker +5 more
semanticscholar +1 more source