AIMC Topic: Deep Learning

Clear Filters Showing 25241 to 25250 of 28423 articles

Unveil cis-acting combinatorial mRNA motifs by interpreting deep neural network.

Bioinformatics (Oxford, England)
SUMMARY: Cis-acting mRNA elements play a key role in the regulation of mRNA stability and translation efficiency. Revealing the interactions of these elements and their impact plays a crucial role in understanding the regulation of the mRNA translati...

scGrapHiC: deep learning-based graph deconvolution for Hi-C using single cell gene expression.

Bioinformatics (Oxford, England)
SUMMARY: Single-cell Hi-C (scHi-C) protocol helps identify cell-type-specific chromatin interactions and sheds light on cell differentiation and disease progression. Despite providing crucial insights, scHi-C data is often underutilized due to the hi...

Deep learning in the diagnosis for cystic lesions of the jaws: a review of recent progress.

Dento maxillo facial radiology
Cystic lesions of the gnathic bones present challenges in differential diagnosis. In recent years, artificial intelligence (AI) represented by deep learning (DL) has rapidly developed and emerged in the field of dental and maxillofacial radiology (DM...

Automated vessel-specific coronary artery calcification quantification with deep learning in a large multi-centre registry.

European heart journal. Cardiovascular Imaging
AIMS: Vessel-specific coronary artery calcification (CAC) is additive to global CAC for prognostic assessment. We assessed accuracy and prognostic implications of vessel-specific automated deep learning (DL) CAC analysis on electrocardiogram (ECG) ga...

From data to diagnosis: skin cancer image datasets for artificial intelligence.

Clinical and experimental dermatology
Artificial intelligence (AI) solutions for skin cancer diagnosis continue to gain momentum, edging closer towards broad clinical use. These AI models, particularly deep-learning architectures, require large digital image datasets for development. Thi...

Classifying real-world macroscopic images in the primary-secondary care interface using transfer learning: implications for development of artificial intelligence solutions using nondermoscopic images.

Clinical and experimental dermatology
BACKGROUND: The application of deep learning (DL) to diagnostic dermatology has been the subject of numerous studies, with some reporting skin lesion classification performance on curated datasets comparable to that of experienced dermatologists. Mos...

ChatGPT versus clinician: challenging the diagnostic capabilities of artificial intelligence in dermatology.

Clinical and experimental dermatology
BACKGROUND: ChatGPT is an online language-based platform designed to answer questions in a human-like way, using deep learning -technology.

Species-specific design of artificial promoters by transfer-learning based generative deep-learning model.

Nucleic acids research
Native prokaryotic promoters share common sequence patterns, but are species dependent. For understudied species with limited data, it is challenging to predict the strength of existing promoters and generate novel promoters. Here, we developed Promo...

Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

Medicine
The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoco...

[A deep blur learning-based motion artifact reduction algorithm for dental cone-beam computed tomography images].

Nan fang yi ke da xue xue bao = Journal of Southern Medical University
OBJECTIVE: We propose a motion artifact correction algorithm (DMBL) for reducing motion artifacts in reconstructed dental cone-beam computed tomography (CBCT) images based on deep blur learning.