AIMC Topic: Deep Learning

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De-identifying Australian hospital discharge summaries: An end-to-end framework using ensemble of deep learning models.

Journal of biomedical informatics
Electronic Medical Records (EMRs) contain clinical narrative text that is of great potential value to medical researchers. However, this information is mixed with Personally Identifiable Information (PII) that presents risks to patient and clinician ...

Single-sequence protein structure prediction using a language model and deep learning.

Nature biotechnology
AlphaFold2 and related computational systems predict protein structure using deep learning and co-evolutionary relationships encoded in multiple sequence alignments (MSAs). Despite high prediction accuracy achieved by these systems, challenges remain...

Deep Learning for Chondrogenic Tumor Classification through Wavelet Transform of Raman Spectra.

Sensors (Basel, Switzerland)
The grading of cancer tissues is still one of the main challenges for pathologists. The development of enhanced analysis strategies hence becomes crucial to accurately identify and further deal with each individual case. Raman spectroscopy (RS) is a ...

Detecting and Analyzing Suicidal Ideation on Social Media Using Deep Learning and Machine Learning Models.

International journal of environmental research and public health
Individuals who suffer from suicidal ideation frequently express their views and ideas on social media. Thus, several studies found that people who are contemplating suicide can be identified by analyzing social media posts. However, finding and comp...

Prediction of Epstein-Barr Virus Status in Gastric Cancer Biopsy Specimens Using a Deep Learning Algorithm.

JAMA network open
IMPORTANCE: Epstein-Barr virus (EBV)-associated gastric cancer (EBV-GC) is 1 of 4 molecular subtypes of GC and is confirmed by an expensive molecular test, EBV-encoded small RNA in situ hybridization. EBV-GC has 2 histologic characteristics, lymphoid...

Deep learning-based automatic-bone-destruction-evaluation system using contextual information from other joints.

Arthritis research & therapy
BACKGROUND: X-ray images are commonly used to assess the bone destruction of rheumatoid arthritis. The purpose of this study is to propose an automatic-bone-destruction-evaluation system fully utilizing deep neural networks (DNN). This system detects...

Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces.

Nature communications
A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalabilit...

AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.

Computational intelligence and neuroscience
Tuberculosis (TB) is an airborne disease caused by . It is imperative to detect cases of TB as early as possible because if left untreated, there is a 70% chance of a patient dying within 10 years. The necessity for supplementary tools has increased ...

How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10-14 in ultrasound using deep learning.

Computer methods and programs in biomedicine
PURPOSE: To investigate if artificial intelligence can identify fetus intracranial structures in pregnancy week 11-14; to provide an automated method of standard and non-standard sagittal view classification in obstetric ultrasound examination METHOD...

Ultrafast lumbar spine MRI protocol using deep learning-based reconstruction: diagnostic equivalence to a conventional protocol.

Skeletal radiology
OBJECTIVE: To evaluate the diagnostic equivalency between an ultrafast (1 min 53 s) lumbar MRI protocol using deep learning-based reconstruction and a conventional lumbar MRI protocol (12 min 31 s).