AIMC Topic: Neural Networks, Computer

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Convolutional Invasion and Expansion Networks for Tumor Growth Prediction.

IEEE transactions on medical imaging
Tumor growth is associated with cell invasion and mass-effect, which are traditionally formulated by mathematical models, namely reaction-diffusion equations and biomechanics. Such models can be personalized based on clinical measurements to build th...

Automatic Calcium Scoring in Low-Dose Chest CT Using Deep Neural Networks With Dilated Convolutions.

IEEE transactions on medical imaging
Heavy smokers undergoing screening with low-dose chest CT are affected by cardiovascular disease as much as by lung cancer. Low-dose chest CT scans acquired in screening enable quantification of atherosclerotic calcifications and thus enable identifi...

An On-Chip Learning Neuromorphic Autoencoder With Current-Mode Transposable Memory Read and Virtual Lookup Table.

IEEE transactions on biomedical circuits and systems
This paper presents an IC implementation of on-chip learning neuromorphic autoencoder unit in a form of rate-based spiking neural network. With a current-mode signaling scheme embedded in a 500 × 500 6b SRAM-based memory, the proposed architecture ac...

PERSON-Personalized Expert Recommendation System for Optimized Nutrition.

IEEE transactions on biomedical circuits and systems
The rise of personalized diets is due to the emergence of nutrigenetics and genetic tests services. However, the recommendation system is far from mature to provide personalized food suggestion to consumers for daily usage. The main barrier of connec...

A Scalable Multicore Architecture With Heterogeneous Memory Structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs).

IEEE transactions on biomedical circuits and systems
Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electro...

Artificial neural networks and liver transplantation: Are we ready for self-driving cars?

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society

Real-time cerebellar neuroprosthetic system based on a spiking neural network model of motor learning.

Journal of neural engineering
OBJECTIVE: Damage to the brain, as a result of various medical conditions, impacts the everyday life of patients and there is still no complete cure to neurological disorders. Neuroprostheses that can functionally replace the damaged neural circuit h...

Validation of artificial neural networks as a methodology for donor-recipient matching for liver transplantation.

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
In 2014, we reported a model for donor-recipient (D-R) matching in liver transplantation (LT) based on artificial neural networks (ANNs) from a Spanish multicenter study (Model for Allocation of Donor and Recipient in España [MADR-E]). The aim is to ...

Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach.

Psychonomic bulletin & review
Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed represent...

Parallel lensless compressive imaging via deep convolutional neural networks.

Optics express
We report a parallel lensless compressive imaging system, which enjoys real-time reconstruction using deep convolutional neural networks. A prototype composed of a low-cost LCD, 16 photo-diodes and isolation chambers, has been built. Each of these 16...