Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation problems. However, the success of existing methods has highly relied on huge computational complexity and massive storage, which is impractical in the real-world scenario. To deal with this problem, we propose an efficient architecture by distilling kno...
Semantic segmentation, as a pixel-level recognition task, has been widely used in a variety of practical scenes. Most of the existing methods try to improve the performance of the network by fusing the information of high and low layers. This kind of simple concatenation or element-wise addition will lead to the problem of unbalanced fusion and low utilization of inter-level features. To solve thi...
PURPOSE: Recently, two-dimensional-to-three-dimensional (2D-3D) deformable registration has been applied to deform liver tumor contours from prior ref...
Deep learning-based applications have great potential to enhance the quality of medical services. The power of deep learning depends on open databases...
Disease prediction is a well-known classification problem in medical applications. Graph Convolutional Networks (GCNs) provide a powerful tool for ana...
This article develops an adaptive neural-network (NN) boundary control scheme for a flexible manipulator subject to input constraints, model uncertain...
Most traditional superpixel segmentation methods used binary logic to generate superpixels for natural images. When these methods are used for images ...
Recommender systems offer several advantages to hospital data management units and patients with special needs. These systems are more dependent on th...
Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease ...
Computational capabilities are rapidly increasing, primarily because of the availability of GPU-based architectures. This creates unprecedented simula...
Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various envi...
This paper proposes a fully automatic method to segment the inner boundary of the bony orbit in two different image modalities: magnetic resonance ima...
Artificial intelligence (AI) in clinical medicine includes physical robotics and devices and virtual AI and machine learning. Concerns have been raise...
Protein domains are independent, functional, and stable structural units of proteins. Accurate protein domain boundary prediction plays an important r...
Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be ...
With the advance of deep learning technology, convolutional neural network (CNN) has been wildly used and achieved the state-of-the-art performances i...
This paper presents a collaborative obstacle avoidance algorithm of multiple bionic snake robots in fluid based on IB-LBM. The method can make the mul...
PURPOSE: The most direct means of glaucoma screening is to use cup-to-disc ratio via colour fundus photography, the first step of which is the precise...
In this paper, we propose a method to enhance the performance of segmentation models for medical images. The method is based on convolutional neural n...
Three-dimensional in vitro tumor models provide more physiologically relevant responses to drugs than 2D models, but the lack of proper evaluation ind...