AIMC Topic: Neural Networks, Computer

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CS-Net: Deep learning segmentation of curvilinear structures in medical imaging.

Medical image analysis
Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the...

Improved approach to the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks based on new inequalities.

Neural networks : the official journal of the International Neural Network Society
This paper studies the problem of the global Mittag-Leffler synchronization for fractional-order multidimension-valued BAM neural networks (FOMVBAMNNs) with general activation functions (AFs). First, the unified model is established for the researche...

Unifying neural learning and symbolic reasoning for spinal medical report generation.

Medical image analysis
Automated medical report generation in spine radiology, i.e., given spinal medical images and directly create radiologist-level diagnosis reports to support clinical decision making, is a novel yet fundamental study in the domain of artificial intell...

Learning interaction dynamics with an interactive LSTM for conversational sentiment analysis.

Neural networks : the official journal of the International Neural Network Society
Conversational sentiment analysis is an emerging, yet challenging subtask of the sentiment analysis problem. It aims to discover the affective state and sentimental change in each person in a conversation based on their opinions. There exists a wealt...

High-Performance Organic Electrochemical Transistors with Nanoscale Channel Length and Their Application to Artificial Synapse.

ACS applied materials & interfaces
Organic electrochemical transistors (OECTs) have attracted considerable interests for various applications ranging from biosensors to digital logic circuits and artificial synapses. However, the majority of reported OECTs utilize large channel length...

Predicting voxel-level dose distributions for esophageal radiotherapy using densely connected network with dilated convolutions.

Physics in medicine and biology
This work aims to develop a voxel-level dose prediction framework by integrating distance information between PTV and OARs, as well as image information, into a densely-connected network (DCNN). Firstly, a four-channel feature map, consisting of a PT...

Spiking neural state machine for gait frequency entrainment in a flexible modular robot.

PloS one
We propose a modular architecture for neuromorphic closed-loop control based on bistable relaxation oscillator modules consisting of three spiking neurons each. Like its biological prototypes, this basic component is robust to parameter variation but...

[Not Available].

Zeitschrift fur medizinische Physik
BACKGROUND: Currently there is an ever increasing interest in Lu-177 targeted radionuclide therapies, which target neuro-endocrine and prostate tumours. For a patient-specific treatment, an individual dosimetry based on SPECT/CT imaging is necessary....

Meaning maps and saliency models based on deep convolutional neural networks are insensitive to image meaning when predicting human fixations.

Cognition
Eye movements are vital for human vision, and it is therefore important to understand how observers decide where to look. Meaning maps (MMs), a technique to capture the distribution of semantic information across an image, have recently been proposed...

An EM Algorithm for Capsule Regression.

Neural computation
We investigate a latent variable model for multinomial classification inspired by recent capsule architectures for visual object recognition (Sabour, Frosst, & Hinton, 2017). Capsule architectures use vectors of hidden unit activities to encode the p...