AIMC Topic: Deep Learning

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Pheno-Deep Counter: a unified and versatile deep learning architecture for leaf counting.

The Plant journal : for cell and molecular biology
Direct observation of morphological plant traits is tedious and a bottleneck for high-throughput phenotyping. Hence, interest in image-based analysis is increasing, with the requirement for software that can reliably extract plant traits, such as lea...

High-throughput ovarian follicle counting by an innovative deep learning approach.

Scientific reports
The evaluation of the number of mouse ovarian primordial follicles (PMF) can provide important information about ovarian function, regulation of folliculogenesis or the impact of chemotherapy on fertility. This counting, usually performed by speciali...

Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Magnetic resonance in medicine
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability...

BERMP: a cross-species classifier for predicting mA sites by integrating a deep learning algorithm and a random forest approach.

International journal of biological sciences
N-methyladenosine (mA) is a prevalent RNA methylation modification involved in several biological processes. Hundreds or thousands of mA sites identified from different species using high-throughput experiments provides a rich resource to construct ...

Convolutional Neural Networks with Template-Based Data Augmentation for Functional Lung Image Quantification.

Academic radiology
RATIONALE AND OBJECTIVES: We propose an automated segmentation pipeline based on deep learning for proton lung MRI segmentation and ventilation-based quantification which improves on our previously reported methodologies in terms of computational eff...

Deep learning in biomedicine.

Nature biotechnology
Deep learning is beginning to impact biological research and biomedical applications as a result of its ability to integrate vast datasets, learn arbitrarily complex relationships and incorporate existing knowledge. Already, deep learning models can ...

Trainable spectral difference learning with spatial starting for hyperspectral image denoising.

Neural networks : the official journal of the International Neural Network Society
Because of the limited reflected energy and incoming illumination in an individual band, the reflected energy captured by a hyperspectral sensor might be low and there is inevitable noise that significantly decreases the performance of the subsequent...

Development and Validation of a Deep Learning System for Staging Liver Fibrosis by Using Contrast Agent-enhanced CT Images in the Liver.

Radiology
Purpose To develop and validate a deep learning system (DLS) for staging liver fibrosis by using CT images in the liver. Materials and Methods DLS for CT-based staging of liver fibrosis was created by using a development data set that included portal...

Evaluation of automatic video captioning using direct assessment.

PloS one
We present Direct Assessment, a method for manually assessing the quality of automatically-generated captions for video. Evaluating the accuracy of video captions is particularly difficult because for any given video clip there is no definitive groun...

Predicting improved protein conformations with a temporal deep recurrent neural network.

PloS one
Accurate protein structure prediction from amino acid sequence is still an unsolved problem. The most reliable methods centre on template based modelling. However, the accuracy of these models entirely depends on the availability of experimentally re...