AIMC Topic: Humans

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Effective deep learning approaches for predicting COVID-19 outcomes from chest computed tomography volumes.

Scientific reports
The rapid evolution of the novel coronavirus disease (COVID-19) pandemic has resulted in an urgent need for effective clinical tools to reduce transmission and manage severe illness. Numerous teams are quickly developing artificial intelligence appro...

Non-invasively accuracy enhanced blood glucose sensor using shallow dense neural networks with NIR monitoring and medical features.

Scientific reports
Non-invasive and accurate method for continuous blood glucose monitoring, the self-testing of blood glucose is in quest for better diagnosis, control and the management of diabetes mellitus (DM). Therefore, this study reports a multiple photonic band...

A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments.

Nature communications
A genetic knockout can be lethal to one human cell type while increasing growth rate in another. This context specificity confounds genetic analysis and prevents reproducible genome engineering. Genome-wide CRISPR compendia across most common human c...

3D printing of resilient biogels for omnidirectional and exteroceptive soft actuators.

Science robotics
Soft robotics greatly benefits from nature as a source of inspiration, introducing innate means of safe interaction between robotic appliances and living organisms. In contrast, the materials involved are often nonbiodegradable or stem from nonrenewa...

Predicting Embryo Viability Based on Self-Supervised Alignment of Time-Lapse Videos.

IEEE transactions on medical imaging
With self-supervised learning, both labeled and unlabeled data can be used for representation learning and model pretraining. This is particularly relevant when automating the selection of a patient's fertilized eggs (embryos) during a fertility trea...

DIFFnet: Diffusion Parameter Mapping Network Generalized for Input Diffusion Gradient Schemes and b-Value.

IEEE transactions on medical imaging
In MRI, deep neural networks have been proposed to reconstruct diffusion model parameters. However, the inputs of the networks were designed for a specific diffusion gradient scheme (i.e., diffusion gradient directions and numbers) and a specific b-v...

SMU-Net: Saliency-Guided Morphology-Aware U-Net for Breast Lesion Segmentation in Ultrasound Image.

IEEE transactions on medical imaging
Deep learning methods, especially convolutional neural networks, have been successfully applied to lesion segmentation in breast ultrasound (BUS) images. However, pattern complexity and intensity similarity between the surrounding tissues (i.e., back...

Propagating Uncertainty Across Cascaded Medical Imaging Tasks for Improved Deep Learning Inference.

IEEE transactions on medical imaging
Although deep networks have been shown to perform very well on a variety of medical imaging tasks, inference in the presence of pathology presents several challenges to common models. These challenges impede the integration of deep learning models in...

Deploying Machine Learning Techniques for Human Emotion Detection.

Computational intelligence and neuroscience
Emotion recognition is one of the trending research fields. It is involved in several applications. Its most interesting applications include robotic vision and interactive robotic communication. Human emotions can be detected using both speech and v...