AIMC Topic: Deep Learning

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Evaluating body composition by combining quantitative spectral detector computed tomography and deep learning-based image segmentation.

European journal of radiology
PURPOSE: Aim of this study was to develop and evaluate a software toolkit, which allows for a fully automated body composition analysis in contrast enhanced abdominal computed tomography leveraging the strengths of both, quantitative information from...

Deep clustering with a Dynamic Autoencoder: From reconstruction towards centroids construction.

Neural networks : the official journal of the International Neural Network Society
In unsupervised learning, there is no apparent straightforward cost function that can capture the significant factors of variations and similarities. Since natural systems have smooth dynamics, an opportunity is lost if an unsupervised objective func...

Towards explainable deep neural networks (xDNN).

Neural networks : the official journal of the International Neural Network Society
In this paper, we propose an elegant solution that is directly addressing the bottlenecks of the traditional deep learning approaches and offers an explainable internal architecture that can outperform the existing methods, requires very little compu...

Classification of glomerular pathological findings using deep learning and nephrologist-AI collective intelligence approach.

International journal of medical informatics
BACKGROUND: Automated classification of glomerular pathological findings is potentially beneficial in establishing an efficient and objective diagnosis in renal pathology. While previous studies have verified the artificial intelligence (AI) models f...

Fast interactive medical image segmentation with weakly supervised deep learning method.

International journal of computer assisted radiology and surgery
PURPOSE: To achieve accurate image segmentation, which is the first critical step in medical image analysis and interventions, using deep neural networks seems a promising approach provided sufficiently large and diverse annotated data from experts. ...

Evaluation of the VETSCAN IMAGYST: an in-clinic canine and feline fecal parasite detection system integrated with a deep learning algorithm.

Parasites & vectors
BACKGROUND: Fecal examination is an important component of routine companion animal wellness exams. Sensitivity and specificity of fecal examinations, however, are influenced by sample preparation methodologies and the level of training and experienc...

Keras R-CNN: library for cell detection in biological images using deep neural networks.

BMC bioinformatics
BACKGROUND: A common yet still manual task in basic biology research, high-throughput drug screening and digital pathology is identifying the number, location, and type of individual cells in images. Object detection methods can be useful for identif...

NuClick: A deep learning framework for interactive segmentation of microscopic images.

Medical image analysis
Object segmentation is an important step in the workflow of computational pathology. Deep learning based models generally require large amount of labeled data for precise and reliable prediction. However, collecting labeled data is expensive because ...

A deep learning approach based on convolutional LSTM for detecting diabetes.

Computational biology and chemistry
Diabetes is a chronic disease that occurs when the pancreas does not generate sufficient insulin or the body cannot effectively utilize the produced insulin. If it remains unidentified and untreated, then it could be very deadliest. One can lead a he...

MonkeyKing: Adaptive Parameter Tuning on Big Data Platforms with Deep Reinforcement Learning.

Big data
Choosing the right parameter configurations for recurring jobs running on big data analytics platforms is difficult because there can be hundreds of possible parameter configurations to pick from. Even the selection of parameter configurations is bas...