AIMC Topic: Deep Learning

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Automatic kidney segmentation in ultrasound images using subsequent boundary distance regression and pixelwise classification networks.

Medical image analysis
It remains challenging to automatically segment kidneys in clinical ultrasound (US) images due to the kidneys' varied shapes and image intensity distributions, although semi-automatic methods have achieved promising performance. In this study, we pro...

Signal identification system for developing rehabilitative device using deep learning algorithms.

Artificial intelligence in medicine
Paralyzed patients were increasing day by day. Some of the neurodegenerative diseases like amyotrophic lateral sclerosis, Brainstem Leison, Stupor and Muscular dystrophy affect the muscle movements in the body. The affected persons were unable to mig...

Haptic Material Analysis and Classification Inspired by Human Exploratory Procedures.

IEEE transactions on haptics
We present a framework for the acquisition and parametrization of object material properties. The introduced acquisition device, denoted as Texplorer2, is able to extract surface material properties while a human operator is performing exploratory pr...

Computational Approaches to Comics Analysis.

Topics in cognitive science
Comics are complex documents whose reception engages cognitive processes such as scene perception, language processing, and narrative understanding. Possibly because of their complexity, they have rarely been studied in cognitive science. Modeling th...

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma.

BMC medical informatics and decision making
BACKGROUND: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-a...

Deep learning for pollen allergy surveillance from twitter in Australia.

BMC medical informatics and decision making
BACKGROUND: The paper introduces a deep learning-based approach for real-time detection and insights generation about one of the most prevalent chronic conditions in Australia - Pollen allergy. The popular social media platform is used for data colle...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Medical image analysis
Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which results in little to no contrast at the lung boundary. For segmentation of severely injured lungs, loc...

Automated CT-derived skeletal muscle mass determination in lower hind limbs of mice using a 3D U-Net deep learning network.

Journal of applied physiology (Bethesda, Md. : 1985)
The loss of skeletal muscle mass is recognized as a complication of several chronic diseases and is associated with increased mortality and a decreased quality of life. Relevant and reliable animal models in which muscle wasting can be monitored noni...

In-Silico Molecular Binding Prediction for Human Drug Targets Using Deep Neural Multi-Task Learning.

Genes
In in-silico prediction for molecular binding of human genomes, promising results have been demonstrated by deep neural multi-task learning due to its strength in training tasks with imbalanced data and its ability to avoid over-fitting. Although the...