AIMC Topic: Algorithms

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A machine-learning-based alternative to phylogenetic bootstrap.

Bioinformatics (Oxford, England)
MOTIVATION: Currently used methods for estimating branch support in phylogenetic analyses often rely on the classic Felsenstein's bootstrap, parametric tests, or their approximations. As these branch support scores are widely used in phylogenetic ana...

Enhancing Hi-C contact matrices for loop detection with Capricorn: a multiview diffusion model.

Bioinformatics (Oxford, England)
MOTIVATION: High-resolution Hi-C contact matrices reveal the detailed three-dimensional architecture of the genome, but high-coverage experimental Hi-C data are expensive to generate. Simultaneously, chromatin structure analyses struggle with extreme...

Application of machine learning in the preoperative radiomic diagnosis of ameloblastoma and odontogenic keratocyst based on cone-beam CT.

Dento maxillo facial radiology
OBJECTIVES: Preoperative diagnosis of oral ameloblastoma (AME) and odontogenic keratocyst (OKC) has been a challenge in dentistry. This study uses radiomics approaches and machine learning (ML) algorithms to characterize cone-beam CT (CBCT) image fea...

[A medical visual question answering approach based on co-attention networks].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Recent studies have introduced attention models for medical visual question answering (MVQA). In medical research, not only is the modeling of "visual attention" crucial, but the modeling of "question attention" is equally significant. To facilitate ...

[An ensemble model for assisting early Alzheimer's disease diagnosis based on structural magnetic resonance imaging with dual-time-point fusion].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Alzheimer's Disease (AD) is a progressive neurodegenerative disorder. Due to the subtlety of symptoms in the early stages of AD, rapid and accurate clinical diagnosis is challenging, leading to a high rate of misdiagnosis. Current research on early d...

[Research of electrical impedance tomography based on multilayer artificial neural network optimized by Hadamard product for human-chest models].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Electrical impedance tomography (EIT) is a non-radiation, non-invasive visual diagnostic technique. In order to improve the imaging resolution and the removing artifacts capability of the reconstruction algorithms for electrical impedance imaging in ...

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...

[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.