AIMC Topic: Deep Learning

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The 3D reconstructed skin micronucleus assay using imaging flow cytometry and deep learning: A proof-of-principle investigation.

Mutation research. Genetic toxicology and environmental mutagenesis
The reconstructed skin micronucleus (RSMN) assay was developed in 2006, as an in vitro alternative for genotoxicity evaluation of dermally applied chemicals or products. In the years since, significant progress has been made in the optimization of th...

MaskLayer: Enabling scalable deep learning solutions by training embedded feature sets.

Neural networks : the official journal of the International Neural Network Society
Deep learning-based methods have shown to achieve excellent results in a variety of domains, however, some important assets are absent. Quality scalability is one of them. In this work, we introduce a novel and generic neural network layer, named Mas...

A conditional Triplet loss for few-shot learning and its application to image co-segmentation.

Neural networks : the official journal of the International Neural Network Society
Few-shot learning tries to solve the problems that suffer the limited number of samples. In this paper we present a novel conditional Triplet loss for solving few-shot problems using deep metric learning. While the conventional Triplet loss suffers t...

Extracting and inserting knowledge into stacked denoising auto-encoders.

Neural networks : the official journal of the International Neural Network Society
Deep neural networks (DNNs) with a complex structure and multiple nonlinear processing units have achieved great successes for feature learning in image and visualization analysis. Due to interpretability of the "black box" problem in DNNs, however, ...

Applications of deep learning in fundus images: A review.

Medical image analysis
The use of fundus images for the early screening of eye diseases is of great clinical importance. Due to its powerful performance, deep learning is becoming more and more popular in related applications, such as lesion segmentation, biomarkers segmen...

Automatic detection of seafloor marine litter using towed camera images and deep learning.

Marine pollution bulletin
Aerial and underwater imaging is being widely used for monitoring litter objects found at the sea surface, beaches and seafloor. However, litter monitoring requires a considerable amount of human effort, indicating the need for automatic and cost-eff...

Prediction of adverse drug reactions using drug convolutional neural networks.

Journal of bioinformatics and computational biology
Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. ...

Deep Learning for Robust Decomposition of High-Density Surface EMG Signals.

IEEE transactions on bio-medical engineering
Blind source separation (BSS) algorithms, such as gradient convolution kernel compensation (gCKC), can efficiently and accurately decompose high-density surface electromyography (HD-sEMG) signals into constituent motor unit (MU) action potential trai...

Deep Learning Regression for Prostate Cancer Detection and Grading in Bi-Parametric MRI.

IEEE transactions on bio-medical engineering
One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI....

Reference-Driven Undersampled MR Image Reconstruction Using Wavelet Sparsity-Constrained Deep Image Prior.

Computational and mathematical methods in medicine
Deep learning has shown potential in significantly improving performance for undersampled magnetic resonance (MR) image reconstruction. However, one challenge for the application of deep learning to clinical scenarios is the requirement of large, hig...