Latest AI and machine learning research in medical ethics / professional responsibility for healthcare professionals.
In this paper, we propose a geometric neural network with edge-aware refinement (GeoNet++) to jointly predict both depth and surface normal maps from a single image. Building on top of two-stream CNNs, GeoNet++ captures the geometric relationships between depth and surface normals with the proposed depth-to-normal and normal-to-depth modules. In particular, the "depth-to-normal" module exploits th...
Organ segmentation from existing imaging is vital to the medical image analysis and disease diagnosis. However, the boundary shapes and area sizes of the target region tend to be diverse and flexible. And the frequent applications of pooling operations in traditional segmentor result in the loss of spatial information which is advantageous to segmentation. All these issues pose challenges and diff...
In order to investigate the impact of holistic care on line coagulation and safety in hemodialysis and to address limitations of the conventional ultr...
In this paper, we are concerned with the multimode function multistability for Cohen-Grossberg neural networks (CGNNs) with mixed time delays. It is i...
Recent advances have been made in applying convolutional neural networks to achieve more precise prediction results for medical image segmentation pro...
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 i...
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...