AIMC Topic: Image Processing, Computer-Assisted

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A Three-Stage Deep Learning Model for Accurate Retinal Vessel Segmentation.

IEEE journal of biomedical and health informatics
Automatic retinal vessel segmentation is a fundamental step in the diagnosis of eye-related diseases, in which both thick vessels and thin vessels are important features for symptom detection. All existing deep learning models attempt to segment both...

A study of intracortical porosity's area fractions and aspect ratios using computer vision and pulse-coupled neural networks.

Medical & biological engineering & computing
Employing computer vision (CV) and optimized pulse-coupled neural networks (PCNN), this work automatically quantifies the geometrical attributes of intracortical bone porosity (namely lacunae and canaliculi (L-C), Haversian canals, and resorption cav...

Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine
Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease...

Multi-task SonoEyeNet: Detection of Fetal Standardized Planes Assisted by Generated Sonographer Attention Maps.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
We present a novel multi-task convolutional neural network called Multi-task SonoEyeNet () that learns to generate clinically relevant visual attention maps using sonographer gaze tracking data on input ultrasound (US) video frames so as to assist st...

Adversarial Similarity Network for Evaluating Image Alignment in Deep Learning based Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
This paper introduces an unsupervised adversarial similarity network for image registration. Unlike existing deep learning registration frameworks, our approach does not require ground-truth deformations and specific similarity metrics. We connect a ...

A Convolutional Neural Network Uses Microscopic Images to Differentiate between Mouse and Human Cell Lines and Their Radioresistant Clones.

Cancer research
: Artificial intelligence (AI) trained with a convolutional neural network (CNN) is a recent technological advancement. Previously, several attempts have been made to train AI using medical images for clinical applications. However, whether AI can di...

Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies.

Cancer communications (London, England)
BACKGROUND: Due to the occult anatomic location of the nasopharynx and frequent presence of adenoid hyperplasia, the positive rate for malignancy identification during biopsy is low, thus leading to delayed or missed diagnosis for nasopharyngeal mali...

Joint Weakly and Semi-Supervised Deep Learning for Localization and Classification of Masses in Breast Ultrasound Images.

IEEE transactions on medical imaging
We propose a framework for localization and classification of masses in breast ultrasound images. We have experimentally found that training convolutional neural network-based mass detectors with large, weakly annotated datasets presents a non-trivia...

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes.

Scientific reports
Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tir...