AIMC Topic: Deep Learning

Clear Filters Showing 12741 to 12750 of 28423 articles

Interpretable deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts.

The Lancet. Digital health
BACKGROUND: Endometrial cancer can be molecularly classified into POLE, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole...

Diversion Detection in Small-Diameter HDPE Pipes Using Guided Waves and Deep Learning.

Sensors (Basel, Switzerland)
In this paper, we propose a novel technique for the inspection of high-density polyethylene (HDPE) pipes using ultrasonic sensors, signal processing, and deep neural networks (DNNs). Specifically, we propose a technique that detects whether there is ...

Predicting N6-Methyladenosine Sites in Multiple Tissues of Mammals through Ensemble Deep Learning.

International journal of molecular sciences
N6-methyladenosine (mA) is the most abundant within eukaryotic messenger RNA modification, which plays an essential regulatory role in the control of cellular functions and gene expression. However, it remains an outstanding challenge to detect mRNA ...

Characterizing physiological high-frequency oscillations using deep learning.

Journal of neural engineering
Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, HFOs can also be recorded in the healthy brain regions, which complicates the interpretation...

A proof of concept for a deep learning system that can aid embryologists in predicting blastocyst survival after thaw.

Scientific reports
The ability to understand whether embryos survive the thaw process is crucial to transferring competent embryos that can lead to pregnancy. The objective of this study was to develop a proof of concept deep learning model capable of assisting embryol...

Deep learning classification of urinary sediment crystals with optimal parameter tuning.

Scientific reports
The examination of urinary sediment crystals, the sedimentary components of urine, is useful in screening tests, and is always performed in medical examinations. The examination of urinary sediment crystals is typically done by classifying them under...

Capsule robot pose and mechanism state detection in ultrasound using attention-based hierarchical deep learning.

Scientific reports
Ingestible robotic capsules with locomotion capabilities and on-board sampling mechanism have great potential for non-invasive diagnostic and interventional use in the gastrointestinal tract. Real-time tracking of capsule location and operational sta...

Caries detection with tooth surface segmentation on intraoral photographic images using deep learning.

BMC oral health
BACKGROUND: Intraoral photographic images are helpful in the clinical diagnosis of caries. Moreover, the application of artificial intelligence to these images has been attempted consistently. This study aimed to evaluate a deep learning algorithm fo...

Automatic segmentation of hepatic metastases on DWI images based on a deep learning method: assessment of tumor treatment response according to the RECIST 1.1 criteria.

BMC cancer
BACKGROUND: Evaluation of treated tumors according to Response Evaluation Criteria in Solid Tumors (RECIST) criteria is an important but time-consuming task in medical imaging. Deep learning methods are expected to automate the evaluation process and...

A deep learning model based on concatenation approach to predict the time to extract a mandibular third molar tooth.

BMC oral health
BACKGROUND: Assessing the time required for tooth extraction is the most important factor to consider before surgeries. The purpose of this study was to create a practical predictive model for assessing the time to extract the mandibular third molar ...