Automated cardiac coverage assessment in cardiovascular magnetic resonance imaging using an explainable recurrent 3D dual-domain convolutional network.

Background: Cardiovascular magnetic resonance (CMR) imaging has become a modality with superior power for the diagnosis and prognosis of cardiovascular diseases. One of the essential quality controls of CMR images is to investigate the complete cardiac coverage, which is necessary for the volumetric and functional assessment.

Purpose: This study examines the full cardiac coverage using a 3D dual-domain convolutional model and then improves this model using an innovative explainable salient region detection model and a recurrent architecture.

Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection.

This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer's disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing.

A social image recommendation system based on deep reinforcement learning.

Today, due to the expansion of the Internet and social networks, people are faced with a vast amount of dynamic information. To mitigate the issue of information overload, recommender systems have become pivotal by analyzing users' activity histories to discern their interests and preferences. However, most available social image recommender systems utilize a static strategy, meaning they do not adapt to changes in user preferences.

A cascade transformer-based model for 3D dose distribution prediction in head and neck cancer radiotherapy.

. Radiation therapy is one of the primary methods used to treat cancer in the clinic. Its goal is to deliver a precise dose to the planning target volume while protecting the surrounding organs at risk (OARs).

A generalised deep meta-learning model for automated quality control of cardiovascular magnetic resonance images.

Background And Objectives: Cardiovascular magnetic resonance (CMR) imaging is a powerful modality in functional and anatomical assessment for various cardiovascular diseases. Sufficient image quality is essential to achieve proper diagnosis and treatment. A large number of medical images, the variety of imaging artefacts, and the workload of imaging centres are amongst the factors that reveal the necessity of automatic image quality assessment (IQA).

Prediction of Acute Kidney Injury After Cardiac Surgery Using Interpretable Machine Learning.

Background: Acute kidney injury (AKI) is a complication that occurs for various reasons after surgery, especially cardiac surgery. This complication can lead to a prolonged treatment process, increased costs, and sometimes death. Prediction of postoperative AKI can help anesthesiologists to implement preventive and early treatment strategies to reduce the risk of AKI.

Deep learning approaches for automated classification and segmentation of head and neck cancers and brain tumors in magnetic resonance images: a meta-analysis study.

Purpose: Deep learning (DL) has led to widespread changes in automated segmentation and classification for medical purposes. This study is an attempt to use statistical methods to analyze studies related to segmentation and classification of head and neck cancers (HNCs) and brain tumors in MRI images.

Methods: PubMed, Web of Science, Embase, and Scopus were searched to retrieve related studies published from January 2016 to January 2020.

A View Transformation Model Based on Sparse and Redundant Representation for Human Gait Recognition.

Background: Human gait as an effective behavioral biometric identifier has received much attention in recent years. However, there are challenges which reduce its performance. In this work we aim at improving performance of gait systems under variations in view angles, which present one of the major challenges to gait algorithms.

Respiratory Motion Prediction Using Deep Convolutional Long Short-Term Memory Network.

Background: Pulmonary movements during radiation therapy can cause damage to healthy tissues. It is necessary to adapt treatment planning based on tumor motion to avoid damage to healthy tissues. A range of approaches has been proposed to monitor the issue.

An unsupervised hierarchical approach for automatic intra-retinal cyst segmentation in spectral-domain optical coherence tomography images.

Purpose: Intra-retinal cyst (IRC) is a symptom of macular disorders that occurs due to retinal blood vessel damage and fluid leakage to the macula area. These abnormalities are efficiently visualized using optical coherence tomography (OCT) imaging. These patients need to be regularly monitored for the presence and changes of IRC regions.

A Multimodal Fusion Approach for Bullet Identification Systems.

In the field of forensic science, bullet identification is based on the fact that firing the cartridge from a barrel leaves exclusive microscopic striation on the fired bullets as the fingerprint of the firearm. The bullet identification methods are categorized in 2-D and 3-D based on their image acquisition techniques. In this study, we focus on 2-D optical images using a multimodal technique and propose several distinct methods as its modalities.

Automatic segmentation of abnormal capillary nonperfusion regions in optical coherence tomography angiography images using marker-controlled watershed algorithm.

Diabetic retinopathy (DR) is one of the most complications of diabetes. It is a progressive disease leading to significant vision loss in the patients. Abnormal capillary nonperfusion (CNP) regions are one of the important characteristics of DR increasing with its progression.

Cross-platform and cross-interaction study of user personality based on images on Twitter and Flickr.

Assessing the predictive value of different social media platforms is important to understand the variation in how users reveal themselves across multiple platforms. Most social media platforms allow users to interact in multiple ways: by posting content to the platform, liking others' posts, or building a user profile. While prior studies offer insights into how language use differs across platforms, differences in image usage is less well understood.

A correlation based bullet identification method using empirical mode decomposition.

The striations on bullet surface are 3D micro structures formed when a bullet is forcing its way out of barrel. Each barrel leaves individual striation patterns on bullets. Hence, the striation information of bullets is helpful for firearm identification.

Blind Detection of Region Duplication Forgery Using Fractal Coding and Feature Matching.

Digital image forgery detection is important because of its wide use in applications such as medical diagnosis, legal investigations, and entertainment. Copy-move forgery is one of the famous techniques, which is used in region duplication. Many of the existing copy-move detection algorithms cannot effectively blind detect duplicated regions that are made by powerful image manipulation software like Photoshop.

A New Approach for Copy-Move Detection Based on Improved Weber Local Descriptor.

One of the most common image tampering techniques is copy-move; in this technique, one or more parts of the image are copied and pasted in another area of the image. Recently, various methods have been proposed for copy-move detection; however, many of these techniques are not robust to additional changes like geometric transformation, and they are failed to be useful for detecting small copied areas. In this paper, a new method based on point descriptors which are derived from the integration of textural feature-based Weber law and statistical features of the image is presented.

A robust color image watermarking technique using modified Imperialist Competitive Algorithm.

In this paper, a novel robust watermarking technique using Imperialistic Competition Algorithm (ICA) in the spatial domain is proposed to protect the intellectual property rights of color images. The proposed method tries to insert the watermark in the blocks which are selected by Modified ICA. In this method, ICA has been customized for watermarking.

SOMM: A new service oriented middleware for generic wireless multimedia sensor networks based on code mobility.

Although much research in the area of Wireless Multimedia Sensor Networks (WMSNs) has been done in recent years, the programming of sensor nodes is still time-consuming and tedious. It requires expertise in low-level programming, mainly because of the use of resource constrained hardware and also the low level API provided by current operating systems. The code of the resulting systems has typically no clear separation between application and system logic.

High-Resolution Images with Minimum Energy Dissipation and Maximum Field-of-View in Camera-Based Wireless Multimedia Sensor Networks.

High-resolution images with wide field of view are important in realizing many applications of wireless multimedia sensor networks. Previous works that generally use multi-tier topology and provide such images by increasing the capabilities of camera sensor nodes lead to an increase in network cost. On the other hand, the resulting energy consumption is a considerable issue that has not been seriously considered in previous works.