Results 31 to 40 of about 340,403 (177)
Pixel Diffuser: Practical Interactive Medical Image Segmentation without Ground Truth
Bioengineering, 2023 Medical image segmentation is essential for doctors to diagnose diseases and manage patient status. While deep learning has demonstrated potential in addressing segmentation challenges within the medical domain, obtaining a substantial amount of data ...
Mingeon Ju +5 more
doaj +1 more source
, 2018 Uncertainty estimation methods are expected to improve the understanding and quality of computer-assisted methods used in medical applications (e.g., neurosurgical interventions, radiotherapy planning), where automated medical image segmentation is ...
Blatti-Moreno, Marcela +6 more
core +1 more source
Current Directions in Biomedical Engineering, 2017 Multimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential.
Stich Manuel +3 more
doaj +1 more source
Application of Image Segmentation Technology Based on Machine Learning in Medical Image Analysis [PDF]
ITM Web of ConferencesMedical image analysis heavily relies on the crucial step of image segmentation, which possesses the capability to discern and differentiate various structures within medical imagery.
Zhang Yurun
doaj +1 more source
UNet++: A Nested U-Net Architecture for Medical Image Segmentation
, 2018 In this paper, we present UNet++, a new, more powerful architecture for medical image segmentation. Our architecture is essentially a deeply-supervised encoder-decoder network where the encoder and decoder sub-networks are connected through a series of ...
Liang, Jianming +3 more
core +1 more source
Rethinking Breast Cancer Diagnosis through Deep Learning Based Image Recognition
Sensors, 2023 This paper explored techniques for diagnosing breast cancer using deep learning based medical image recognition. X-ray (Mammography) images, ultrasound images, and histopathology images are used to improve the accuracy of the process by diagnosing breast
Deawon Kwak, Jiwoo Choi, Sungjin Lee
doaj +1 more source
Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation
, 2019 Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision.
Ahmad, Sahar +7 more
core +1 more source
Sensors, 2022 The latest medical image segmentation methods uses UNet and transformer structures with great success. Multiscale feature fusion is one of the important factors affecting the accuracy of medical image segmentation. Existing transformer-based UNet methods
Shaolong Chen +3 more
doaj +1 more source
A review: Deep learning for medical image segmentation using multi-modality fusion
Array, 2019 Multi-modality is widely used in medical imaging, because it can provide multiinformation about a target (tumor, organ or tissue). Segmentation using multimodality consists of fusing multi-information to improve the segmentation.
Tongxue Zhou, Su Ruan, Stéphane Canu
doaj +1 more source