AIMC Topic: Deep Learning

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Minimizing Global Buffer Access in a Deep Learning Accelerator Using a Local Register File with a Rearranged Computational Sequence.

Sensors (Basel, Switzerland)
We propose a method for minimizing global buffer access within a deep learning accelerator for convolution operations by maximizing the data reuse through a local register file, thereby substituting the local register file access for the power-hungry...

Deep Learning and Microscopic Imaging in the Nursing Process of Neurosurgery Operation.

Journal of healthcare engineering
Neurosurgery is mainly for the treatment of head trauma, cerebrovascular disease, brain tumors, and spinal cord disorders. These operations are difficult and risky, so disability and mortality are high. To reduce the risk of surgery, reduce postopera...

A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.

Journal of healthcare engineering
Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artif...

Assessing Schizophrenia Patients Through Linguistic and Acoustic Features Using Deep Learning Techniques.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Thought, language, and communication disorders are among the salient characteristics of schizophrenia. Such impairments are often exhibited in patients' conversations. Researches have shown that assessments of thought disorder are crucial for trackin...

Bayesian deep learning outperforms clinical trial estimators of intracerebral and intraventricular hemorrhage volume.

Journal of neuroimaging : official journal of the American Society of Neuroimaging
BACKGROUND AND PURPOSE: Intracerebral hemorrhage (ICH) and intraventricular hemorrhage (IVH) clinical trials rely on manual linear and semi-quantitative (LSQ) estimators like the ABC/2, modified Graeb and IVH scores for timely volumetric estimation f...

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

Computers in biology and medicine
BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious ...

Deep Learning Approach to UAV Detection and Classification by Using Compressively Sensed RF Signal.

Sensors (Basel, Switzerland)
Recently, the frequent occurrence of the misuse and intrusion of UAVs has made it a research challenge to identify and detect them effectively, and relatively high bandwidth and pressure on data transmission and real-time processing exist when sampli...

Deep learning-based single image super-resolution for low-field MR brain images.

Scientific reports
Low-field MRI scanners are significantly less expensive than their high-field counterparts, which gives them the potential to make MRI technology more accessible all around the world. In general, images acquired using low-field MRI scanners tend to b...

Deep Transfer Learning for Automatic Prediction of Hemorrhagic Stroke on CT Images.

Computational and mathematical methods in medicine
Intracerebral hemorrhage (ICH) is the most common type of hemorrhagic stroke which occurs due to ruptures of weakened blood vessel in brain tissue. It is a serious medical emergency issues that needs immediate treatment. Large numbers of noncontrast-...

Deep Learning-Based Medical Information System in First Aid of Surgical Trauma.

Computational and mathematical methods in medicine
The aim of this study was to explore the application of process reengineering integration in trauma first aid based on deep learning and medical information system. According to the principles and methods of process reengineering, based on the analys...