AIMC Topic: Image Interpretation, Computer-Assisted
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A Self-Supervised Learning Based Framework for Eyelid Malignant Melanoma Diagnosis in Whole Slide Images.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 9, 2024
Eyelid malignant melanoma (MM) is a rare disease with high mortality. Accurate diagnosis of such disease is important but challenging. In clinical practice, the diagnosis of MM is currently performed manually by pathologists, which is subjective and ...
Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 8, 2024
BACKGROUND: Four-dimensional cardiovascular magnetic resonance flow imaging (4D flow CMR) plays an important role in assessing cardiovascular diseases. However, the manual or semi-automatic segmentation of aortic vessel boundaries in 4D flow data int...
Image Interpretation, Computer-Assisted
Adult
Reproducibility of Results
Case-Control Studies
Male
Blood Flow Velocity
Aortic Valve
Hemodynamics
Middle Aged
Bicuspid Aortic Valve Disease
Aorta
Humans
Predictive Value of Tests
Deep Learning
Neural Networks, Computer
Perfusion Imaging
Regional Blood Flow
Female
Aged
Young Adult
Datasets as Topic
Deep learning MR reconstruction in knees and ankles in children and young adults. Is it ready for clinical use?
Skeletal radiology
Aug 8, 2024
OBJECTIVE: To evaluate the diagnostic performance and image quality of accelerated Turbo Spin Echo sequences using deep-learning (DL) reconstructions compared to conventional sequences in knee and ankle MRIs of children and young adults.
BrainSegFounder: Towards 3D foundation models for neuroimage segmentation.
Medical image analysis
Aug 8, 2024
The burgeoning field of brain health research increasingly leverages artificial intelligence (AI) to analyze and interpret neuroimaging data. Medical foundation models have shown promise of superior performance with better sample efficiency. This wor...
PolypNextLSTM: a lightweight and fast polyp video segmentation network using ConvNext and ConvLSTM.
International journal of computer assisted radiology and surgery
Aug 8, 2024
PURPOSE: Commonly employed in polyp segmentation, single-image UNet architectures lack the temporal insight clinicians gain from video data in diagnosing polyps. To mirror clinical practices more faithfully, our proposed solution, PolypNextLSTM, leve...
Artificial Intelligence and Blockchain Enabled Smart Healthcare System for Monitoring and Detection of COVID-19 in Biomedical Images.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Millions of individuals around the world have been impacted by the ongoing coronavirus outbreak, known as the COVID-19 pandemic. Blockchain, Artificial Intelligence (AI), and other cutting-edge digital and innovative technologies have all offered pro...
Big Data Analytics on Lung Cancer Diagnosis Framework With Deep Learning.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
As the segment of diseased tissue in PET images is time-consuming, laborious and low accuracy, this work proposes an automated framework for PET image screening, denoising and diseased tissue segmentation. First, taking into account the characteristi...
Deep Factor Learning for Accurate Brain Neuroimaging Data Analysis on Discrimination for Structural MRI and Functional MRI.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Analysis of neuroimaging data (e.g., Magnetic Resonance Imaging, structural and functional MRI) plays an important role in monitoring brain dynamics and probing brain structures. Neuroimaging data are multi-featured and non-linear by nature, and it i...
An Automated Framework for Histopathological Nucleus Segmentation With Deep Attention Integrated Networks.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Clinical management and accurate disease diagnosis are evolving from qualitative stage to the quantitative stage, particularly at the cellular level. However, the manual process of histopathological analysis is lab-intensive and time-consuming. Meanw...
A YOLOX-Based Deep Instance Segmentation Neural Network for Cardiac Anatomical Structures in Fetal Ultrasound Images.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Echocardiography is an essential procedure for the prenatal examination of the fetus for congenital heart disease (CHD). Accurate segmentation of key anatomical structures in a four-chamber view is an essential step in measuring fetal growth paramete...
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