AIMC Topic: Neural Networks, Computer

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Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more infor...

MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning.

Medical image analysis
Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automa...

MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons.

Computers in biology and medicine
Precise determination and assessment of bladder cancer (BC) extent of muscle invasion involvement guides proper risk stratification and personalized therapy selection. In this context, segmentation of both bladder walls and cancer are of pivotal impo...

Artificial neural networks in tandem with molecular descriptors as predictive tools for continuous liposome manufacturing.

International journal of pharmaceutics
The current study utilized an artificial neural network (ANN) to generate computational models to achieve process optimization for a previously developed continuous liposome manufacturing system. The liposome formation was based on a continuous manuf...

A deep learning model for detection of cervical spinal cord compression in MRI scans.

Scientific reports
Magnetic Resonance Imaging (MRI) evidence of spinal cord compression plays a central role in the diagnosis of degenerative cervical myelopathy (DCM). There is growing recognition that deep learning models may assist in addressing the increasing volum...

A CNN identified by reinforcement learning-based optimization framework for EEG-based state evaluation.

Journal of neural engineering
Electroencephalogram (EEG) data, as a kind of complex time-series, is one of the most widely-used information measurements for evaluating human psychophysiological states. Recently, numerous works applied deep learning techniques, especially the conv...

Protein transfer learning improves identification of heat shock protein families.

PloS one
Heat shock proteins (HSPs) play a pivotal role as molecular chaperones against unfavorable conditions. Although HSPs are of great importance, their computational identification remains a significant challenge. Previous studies have two major limitati...

Streaming cascade-based speech translation leveraged by a direct segmentation model.

Neural networks : the official journal of the International Neural Network Society
The cascade approach to Speech Translation (ST) is based on a pipeline that concatenates an Automatic Speech Recognition (ASR) system followed by a Machine Translation (MT) system. Nowadays, state-of-the-art ST systems are populated with deep neural ...

Deep learning neural network tools for proteomics.

Cell reports methods
Mass-spectrometry-based proteomics enables quantitative analysis of thousands of human proteins. However, experimental and computational challenges restrict progress in the field. This review summarizes the recent flurry of machine-learning strategie...