AIMC Topic: Deep Learning

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Classifying fungi biodiversity using hybrid transformer models.

Journal of microbiological methods
Fungi are essential members of ecosystems, playing key roles in nutrient cycling, agriculture, and medicine. Their classification into proper species helps us to understand their biodiversity, allowing us to leverage their ecological and practical be...

ECG synthesis for cardiac arrhythmias: Integrating self-supervised learning and generative adversarial networks.

Artificial intelligence in medicine
Arrhythmia classifiers relying on supervised deep learning models usually require a substantial amount of labeled clinical data. The distribution of these labels is strictly related to the statistics of cardiovascular diseases among the population, w...

A Mixed-attention Network for Automated Interventricular Septum Segmentation in Bright-blood Myocardial T2* MRI Relaxometry in Thalassemia.

Academic radiology
RATIONALE AND OBJECTIVES: This study develops a deep-learning method for automatic segmentation of the interventricular septum (IS) in MR images to measure myocardial T2* and estimate cardiac iron deposition in patients with thalassemia.

A Study on Predicting the Efficacy of Posterior Lumbar Interbody Fusion Surgery Using a Deep Learning Radiomics Model.

Academic radiology
RATIONALE AND OBJECTIVES: This study seeks to develop a combined model integrating clinical data, radiomics, and deep learning (DL) for predicting the efficacy of posterior lumbar interbody fusion (PLIF) surgery.

DeepMS: super-fast peptide identification using end-to-end deep learning method.

Journal of molecular biology
Mass spectrometry (MS) has emerged as a powerful omics analysis technique, particularly in proteomics, where the initial step involves identifying MS spectra as peptide sequences. However, this process often requires substantial computational resourc...

Deep Learning CAIPIRINHA-VIBE Improves and Accelerates Head and Neck MRI.

Academic radiology
RATIONALE AND OBJECTIVES: The aim of this study was to evaluate image quality for contrast-enhanced (CE) neck MRI with a deep learning-reconstructed VIBE sequence with acceleration factors (AF) 4 (DL4-VIBE) and 6 (DL6-VIBE).

A deep learning model for accurate segmentation of the Drosophila melanogaster brain from Micro-CT imaging.

Developmental biology
The use of microcomputed tomography (Micro-CT) for imaging biological samples has burgeoned in the past decade, due to increased access to scanning platforms, ease of operation, and the advance of software platforms that enable accurate microstructur...

Deep Learning Differentiates Papilledema, NAION, and Healthy Eyes With Unsegmented 3D OCT Volumes.

American journal of ophthalmology
OBJECTIVE: Deep learning (DL) has been used to differentiate papilledema from healthy eyes and optic disc elevation on fundus photos. As we described optic nerve head (ONH) and peripapillary retina (PPR) optical coherence tomography (OCT) features th...

ICD code mapping model based on clinical text tree structure.

Artificial intelligence in medicine
With the rapid development and progress of big data and artificial intelligence technology, the ICD coding problem of electronic medical records has been effectively solved. The deep learning method, which replaces the manual coding method, has impro...

Deep learning model for malignancy prediction of TI-RADS 4 thyroid nodules with high-risk characteristics using multimodal ultrasound: A multicentre study.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
The automatic screening of thyroid nodules using computer-aided diagnosis holds great promise in reducing missed and misdiagnosed cases in clinical practice. However, most current research focuses on single-modal images and does not fully leverage th...