AIMC Topic: Neural Networks, Computer

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Evaluate the Malignancy of Pulmonary Nodules Using the 3-D Deep Leaky Noisy-OR Network.

IEEE transactions on neural networks and learning systems
Automatic diagnosing lung cancer from computed tomography scans involves two steps: detect all suspicious lesions (pulmonary nodules) and evaluate the whole-lung/pulmonary malignancy. Currently, there are many studies about the first step, but few ab...

Real-time tracking of surgical instruments based on spatio-temporal context and deep learning.

Computer assisted surgery (Abingdon, England)
ABSTARCT Real-time tool tracking in minimally invasive-surgery (MIS) has numerous applications for computer-assisted interventions (CAIs). Visual tracking approaches are a promising solution to real-time surgical tool tracking, however, many approach...

Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentation.

IEEE journal of biomedical and health informatics
Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central regi...

Emergence of cognitive priming and structure building from the hierarchical interaction of canonical microcircuit models.

Biological cybernetics
The concept of connectionism states that higher cognitive functions emerge from the interaction of many simple elements. Accordingly, research on canonical microcircuits conceptualizes findings on fundamental neuroanatomical circuits as well as recur...

[Neural network: A future in pathology?].

Annales de pathologie
Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models...

Gated Orthogonal Recurrent Units: On Learning to Forget.

Neural computation
We present a novel recurrent neural network (RNN)-based model that combines the remembering ability of unitary evolution RNNs with the ability of gated RNNs to effectively forget redundant or irrelevant information in its memory. We achieve this by e...

Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.

Medical physics
PURPOSE: In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in th...

Highly accelerated, model-free diffusion tensor MRI reconstruction using neural networks.

Medical physics
PURPOSE: The purpose of this study was to develop a neural network that accurately performs diffusion tensor imaging (DTI) reconstruction from highly accelerated scans.

Artificial Neural Network for in-Bed Posture Classification Using Bed-Sheet Pressure Sensors.

IEEE journal of biomedical and health informatics
Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video surveillance, the conventional method ...

Constrained-CNN losses for weakly supervised segmentation.

Medical image analysis
Weakly-supervised learning based on, e.g., partially labelled images or image-tags, is currently attracting significant attention in CNN segmentation as it can mitigate the need for full and laborious pixel/voxel annotations. Enforcing high-order (gl...