AIMC Topic: Neural Networks, Computer

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Combining a parallel 2D CNN with a self-attention Dilated Residual Network for CTC-based discrete speech emotion recognition.

Neural networks : the official journal of the International Neural Network Society
A challenging issue in the field of the automatic recognition of emotion from speech is the efficient modelling of long temporal contexts. Moreover, when incorporating long-term temporal dependencies between features, recurrent neural network (RNN) a...

Automated renal segmentation in healthy and chronic kidney disease subjects using a convolutional neural network.

Magnetic resonance in medicine
PURPOSE: Total kidney volume (TKV) is an important measure in renal disease detection and monitoring. We developed a fully automated method to segment the kidneys from T -weighted MRI to calculate TKV of healthy control (HC) and chronic kidney diseas...

MRzero - Automated discovery of MRI sequences using supervised learning.

Magnetic resonance in medicine
PURPOSE: A supervised learning framework is proposed to automatically generate MR sequences and corresponding reconstruction based on the target contrast of interest. Combined with a flexible, task-driven cost function this allows for an efficient ex...

Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Journal of chemical information and modeling
Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite th...

A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer.

Biomolecules
Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a therapeutic target for SCLC. Network pharmacology analysis and virtual screening were utilized to find ou...

A Deep Learning Framework for Recognizing Both Static and Dynamic Gestures.

Sensors (Basel, Switzerland)
Intuitive user interfaces are indispensable to interact with the human centric smart environments. In this paper, we propose a unified framework that recognizes both static and dynamic gestures, using simple RGB vision (without depth sensing). This f...

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence.

BMC medicine
BACKGROUND: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals...

Artificial neural networks for positioning of gamma interactions in monolithic PET detectors.

Physics in medicine and biology
To detect gamma rays with good spatial, timing and energy resolution while maintaining high sensitivity we need accurate and efficient algorithms to estimate the first gamma interaction position from the measured light distribution. Furthermore, mono...

Automating chest radiograph imaging quality control.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: To automate diagnostic chest radiograph imaging quality control (lung inclusion at all four edges, patient rotation, and correct inspiration) using convolutional neural network models.

From convolutional neural networks to models of higher-level cognition (and back again).

Annals of the New York Academy of Sciences
The remarkable successes of convolutional neural networks (CNNs) in modern computer vision are by now well known, and they are increasingly being explored as computational models of the human visual system. In this paper, we ask whether CNNs might al...