AIMC Topic: Neural Networks, Computer

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Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

Journal of digital imaging
Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ ...

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality.

Anesthesiology
WHAT WE ALREADY KNOW ABOUT THIS TOPIC: WHAT THIS ARTICLE TELLS US THAT IS NEW: BACKGROUND:: The authors tested the hypothesis that deep neural networks trained on intraoperative features can predict postoperative in-hospital mortality.

Multi-Modality Cascaded Convolutional Neural Networks for Alzheimer's Disease Diagnosis.

Neuroinformatics
Accurate and early diagnosis of Alzheimer's disease (AD) plays important role for patient care and development of future treatment. Structural and functional neuroimages, such as magnetic resonance images (MRI) and positron emission tomography (PET),...

An Efficient Implementation of Deep Convolutional Neural Networks for MRI Segmentation.

Journal of digital imaging
Image segmentation is one of the most common steps in digital image processing, classifying a digital image into different segments. The main goal of this paper is to segment brain tumors in magnetic resonance images (MRI) using deep learning. Tumors...

Automated Pathogenesis-Based Diagnosis of Lumbar Neural Foraminal Stenosis via Deep Multiscale Multitask Learning.

Neuroinformatics
Pathogenesis-based diagnosis is a key step to prevent and control lumbar neural foraminal stenosis (LNFS). It conducts both early diagnosis and comprehensive assessment by drawing crucial pathological links between pathogenic factors and LNFS. Automa...

Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality.

Neuroinformatics
Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In thi...

Learning Efficient Spatial-Temporal Gait Features with Deep Learning for Human Identification.

Neuroinformatics
The integration of the latest breakthroughs in bioinformatics technology from one side and artificial intelligence from another side, enables remarkable advances in the fields of intelligent security guard computational biology, healthcare, and so on...

Uyghur Text Matching in Graphic Images for Biomedical Semantic Analysis.

Neuroinformatics
How to read Uyghur text from biomedical graphic images is a challenge problem due to the complex layout and cursive writing of Uyghur. In this paper, we propose a system that extracts text from Uyghur biomedical images, and matches the text in a spec...

A deep recurrent neural network discovers complex biological rules to decipher RNA protein-coding potential.

Nucleic acids research
The current deluge of newly identified RNA transcripts presents a singular opportunity for improved assessment of coding potential, a cornerstone of genome annotation, and for machine-driven discovery of biological knowledge. While traditional, featu...