Results 31 to 40 of about 13,515,348 (328)
Channel prior convolutional attention for medical image segmentation [PDF]
Comput. Biol. Medicine, 2023 Characteristics such as low contrast and significant organ shape variations are often exhibited in medical images. The improvement of segmentation performance in medical imaging is limited by the generally insufficient adaptive capabilities of existing ...
He-lu Huang +4 more
semanticscholar +1 more source
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
Evaluating Modelling Approaches for Medical Image Annotations [PDF]
, 2010 Information system designers face many challenges with regards to selecting appropriate semantic technologies and deciding on a modeling approach for their system.
Bijan Parsia +2 more
core +5 more sources
Clinical and MRI Features of Tumors of Septum Pellucidum
Zhongliu Fangzhi Yanjiu, 2018 Objective To investigate the clinical and magnetic resonance imaging (MRI) features of the tumors of septum pellucidum. Methods We analyzed retrospectively the clinical data of 45 patients with the tumors of septum pellucidum confirmed by operation and ...
HAO Ni'na +4 more
doaj +1 more source
European Radiology Experimental, 2020 Background Automated segmentation of anatomical structures is a crucial step in image analysis. For lung segmentation in computed tomography, a variety of approaches exists, involving sophisticated pipelines trained and validated on different datasets ...
Johannes Hofmanninger +5 more
doaj +1 more source
Advanced Medical Image Analysis [PDF]
Computational and Mathematical Methods in Medicine, 2014 Medical image analysis is performed in order to facilitate medical research and ultimately provide better healthcare. It is critical to the advancement of imaging-based medical research, for example, using magnetic resonance (MR) imaging to probe brain structural and functional changes related to a disease or cognitive process.
Rong Chen, Zhongqiu Wang, Yuanjie Zheng
openaire +2 more sources
A State-of-the-Art Survey for Microorganism Image Segmentation Methods and Future Potential
IEEE Access, 2019 Microorganisms play a great role in ecosystem, wastewater treatment, monitoring of environmental changes, and decomposition of waste materials. However, some of them are harmful to humans and animals such as tuberculosis bacteria and plasmodium.
Frank Kulwa +7 more
doaj +1 more source
Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session. [PDF]
, 2017 At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues.
Geis, J Raymond +2 more
core +1 more source
DA-TransUNet: integrating spatial and channel dual attention with transformer U-net for medical image segmentation [PDF]
Frontiers in Bioengineering and Biotechnology, 2023 Accurate medical image segmentation is critical for disease quantification and treatment evaluation. While traditional U-Net architectures and their transformer-integrated variants excel in automated segmentation tasks. Existing models also struggle with
Guanqun Sun +7 more
semanticscholar +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