AIMC Topic: Deep Learning

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Multi-cancer early detection based on serum surface-enhanced Raman spectroscopy with deep learning: a large-scale case-control study.

BMC medicine
BACKGROUND: Early detection of cancer can help patients with more effective treatments and result in better prognosis. Unfortunately, established cancer screening technologies are limited for use, especially for multi-cancer early detection. In this ...

(DA-U)Net: double attention UNet for retinal vessel segmentation.

BMC ophthalmology
BACKGROUND: Morphological changes in the retina are crucial and serve as valuable references in the clinical diagnosis of ophthalmic and cardiovascular diseases. However, the retinal vascular structure is complex, making manual segmentation time-cons...

An ensemble deep learning framework for multi-class LncRNA subcellular localization with innovative encoding strategy.

BMC biology
BACKGROUND: Long non-coding RNA (LncRNA) play pivotal roles in various cellular processes, and elucidating their subcellular localization can offer crucial insights into their functional significance. Accurate prediction of lncRNA subcellular localiz...

Deep learning models for differentiating three sinonasal malignancies using multi-sequence MRI.

BMC medical imaging
PURPOSE: To develop MRI-based deep learning (DL) models for distinguishing sinonasal squamous cell carcinoma (SCC), adenoid cystic carcinoma (ACC) and olfactory neuroblastoma (ONB) and to evaluate whether the DL models could improve the diagnostic pe...

Integrating blockchain technology with artificial intelligence for the diagnosis of tibial plateau fractures.

European journal of trauma and emergency surgery : official publication of the European Trauma Society
PURPOSE: The application of artificial intelligence (AI) in healthcare has seen widespread implementation, with numerous studies highlighting the development of robust algorithms. However, limited attention has been given to the secure utilization of...

Generalizable deep neural networks for image quality classification of cervical images.

Scientific reports
Successful translation of artificial intelligence (AI) models into clinical practice, across clinical domains, is frequently hindered by the lack of image quality control. Diagnostic models are often trained on images with no denotation of image qual...

Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study.

European journal of nuclear medicine and molecular imaging
BACKGROUND: Myocardial perfusion imaging (MPI) using single-photon emission computed tomography (SPECT) is a well-established modality for noninvasive diagnostic assessment of coronary artery disease (CAD). However, the time-consuming and experience-...

Automated Coronary Artery Segmentation with 3D PSPNET using Global Processing and Patch Based Methods on CCTA Images.

Cardiovascular engineering and technology
The prevalence of coronary artery disease (CAD) has become the major cause of death across the world in recent years. The accurate segmentation of coronary artery is important in clinical diagnosis and treatment of coronary artery disease (CAD) such ...

Automatic skull reconstruction by deep learnable symmetry enforcement.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Every year, thousands of people suffer from skull damage and require personalized implants to fill the cranial cavity. Unfortunately, the waiting time for reconstruction surgery can extend to several weeks or even months, es...

Seafloor debris detection using underwater images and deep learning-driven image restoration: A case study from Koh Tao, Thailand.

Marine pollution bulletin
Traditional detection and monitoring of seafloor debris present considerable challenges due to the high costs associated with underwater imaging devices and the complex environmental conditions in marine ecosystems. In response to these challenges, t...