AIMC Topic: Image Interpretation, Computer-Assisted

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Classification of Lung Nodules Based on Deep Residual Networks and Migration Learning.

Computational intelligence and neuroscience
The classification process of lung nodule detection in a traditional computer-aided detection (CAD) system is complex, and the classification result is heavily dependent on the performance of each step in lung nodule detection, causing low classifica...

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence.

Sensors (Basel, Switzerland)
Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such syst...

Three-Dimensional Convolutional Neural Network for Prostate MRI Segmentation and Comparison of Prostate Volume Measurements by Use of Artificial Neural Network and Ellipsoid Formula.

AJR. American journal of roentgenology
The purposes of this study were to assess the performance of a 3D convolutional neural network (CNN) for automatic segmentation of prostates on MR images and to compare the volume estimates from the 3D CNN with those of the ellipsoid formula. The s...

Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI.

IEEE journal of biomedical and health informatics
Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection re...

Review of Stereo Matching Algorithms Based on Deep Learning.

Computational intelligence and neuroscience
Stereo vision is a flourishing field, attracting the attention of many researchers. Recently, leveraging on the development of deep learning, stereo matching algorithms have achieved remarkable performance far exceeding traditional approaches. This r...

Applications of artificial intelligence in multimodality cardiovascular imaging: A state-of-the-art review.

Progress in cardiovascular diseases
There has been a tidal wave of recent interest in artificial intelligence (AI), machine learning and deep learning approaches in cardiovascular (CV) medicine. In the era of modern medicine, AI and electronic health records hold the promise to improve...

Interpatient Similarities in Cardiac Function: A Platform for Personalized Cardiovascular Medicine.

JACC. Cardiovascular imaging
OBJECTIVES: The authors applied unsupervised machine-learning techniques for integrating echocardiographic features of left ventricular (LV) structure and function into a patient similarity network that predicted major adverse cardiac event(s) (MACE)...

Toward a unified framework for interpreting machine-learning models in neuroimaging.

Nature protocols
Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evalu...

Deep Learning for Automated Sorting of Retinal Photographs.

Ophthalmology. Retina
PURPOSE: Though the domain of big data and artificial intelligence in health care continues to evolve, there is a lack of systemic methods to improve data quality and streamline the preparation process. To address this, we aimed to develop an automat...

Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm.

Sensors (Basel, Switzerland)
The detection of objects concealed under people's clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to ...