AI Medical Compendium Topic:
Image Interpretation, Computer-Assisted

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Automatic classification of medical image modality and anatomical location using convolutional neural network.

PloS one
Modern radiologic images comply with DICOM (digital imaging and communications in medicine) standard, which, upon conversion to other image format, would lose its image detail and information such as patient demographics or type of image modality tha...

Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

Nature biomedical engineering
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can...

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.

IEEE/ACM transactions on computational biology and bioinformatics
The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are usin...

Multi-View Mammographic Density Classification by Dilated and Attention-Guided Residual Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Breast density is widely adopted to reflect the likelihood of early breast cancer development. Existing methods of mammographic density classification either require steps of manual operations or achieve only moderate classification accuracy due to t...

MRI Based Radiomics Approach With Deep Learning for Prediction of Vessel Invasion in Early-Stage Cervical Cancer.

IEEE/ACM transactions on computational biology and bioinformatics
This article aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted im...

D-UNet: A Dimension-Fusion U Shape Network for Chronic Stroke Lesion Segmentation.

IEEE/ACM transactions on computational biology and bioinformatics
Assessing the location and extent of lesions caused by chronic stroke is critical for medical diagnosis, surgical planning, and prognosis. In recent years, with the rapid development of 2D and 3D convolutional neural networks (CNN), the encoder-decod...

A Global and Local Enhanced Residual U-Net for Accurate Retinal Vessel Segmentation.

IEEE/ACM transactions on computational biology and bioinformatics
Retinal vessel segmentation is a critical procedure towards the accurate visualization, diagnosis, early treatment, and surgery planning of ocular diseases. Recent deep learning-based approaches have achieved impressive performance in retinal vessel ...

Deep learning-based classification of blue light cystoscopy imaging during transurethral resection of bladder tumors.

Scientific reports
Bladder cancer is one of the top 10 frequently occurring cancers and leads to most cancer deaths worldwide. Recently, blue light (BL) cystoscopy-based photodynamic diagnosis was introduced as a unique technology to enhance the detection of bladder ca...