AIMC Topic: Deep Learning

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Deep scSTAR: leveraging deep learning for the extraction and enhancement of phenotype-associated features from single-cell RNA sequencing and spatial transcriptomics data.

Briefings in bioinformatics
Single-cell sequencing has advanced our understanding of cellular heterogeneity and disease pathology, offering insights into cellular behavior and immune mechanisms. However, extracting meaningful phenotype-related features is challenging due to noi...

DeepRNA-Twist: language-model-guided RNA torsion angle prediction with attention-inception network.

Briefings in bioinformatics
RNA torsion and pseudo-torsion angles are critical in determining the three-dimensional conformation of RNA molecules, which in turn governs their biological functions. However, current methods are limited by RNA's structural complexity as well as fl...

A Novel Deep Learning-based Pathomics Score for Prognostic Stratification in Pancreatic Ductal Adenocarcinoma.

Pancreas
BACKGROUND AND OBJECTIVES: Accurate survival prediction for pancreatic ductal adenocarcinoma (PDAC) is crucial for personalized treatment strategies. This study aims to construct a novel pathomics indicator using hematoxylin and eosin-stained whole s...

Enhancing F-FDG PET image quality and lesion diagnostic performance across different body mass index using the deep progressive learning reconstruction algorithm.

Cancer imaging : the official publication of the International Cancer Imaging Society
BACKGROUND: As body mass index (BMI) increases, the quality of 2-deoxy-2-[fluorine-18]fluoro-D-glucose (F-FDG) positron emission tomography (PET) images reconstructed with ordered subset expectation maximization (OSEM) declines, negatively impacting ...

A hybrid approach for binary and multi-class classification of voice disorders using a pre-trained model and ensemble classifiers.

BMC medical informatics and decision making
Recent advances in artificial intelligence-based audio and speech processing have increasingly focused on the binary and multi-class classification of voice disorders. Despite progress, achieving high accuracy in multi-class classification remains ch...

PhacoTrainer: Automatic Artificial Intelligence-Generated Performance Ratings for Cataract Surgery.

Translational vision science & technology
PURPOSE: To investigate whether cataract surgical skill performance metrics automatically generated by artificial intelligence (AI) models can differentiate between trainee and faculty surgeons and the correlation between AI metrics and expert-rated ...

DDUM: Deformable Dilated U-structure Module for coronary stenosis detection.

Medical engineering & physics
Deep learning methods are increasingly popular in assisting physicians with diagnosing coronary artery disease and reducing errors caused by subjective judgment. However, accessing and labeling medical imaging data, especially coronary angiography da...

Light Bladder Net: Non-invasive Bladder Cancer Prediction by Weighted Deep Learning Approaches and Graphical Data Transformation.

Anticancer research
BACKGROUND/AIM: Bladder cancer (BCa) is associated with high recurrence rates, emphasizing the importance of early and accurate detection. This study aimed to develop a lightweight and fast deep learning model, Light-Bladder-Net (LBN), for non-invasi...

Evaluation of minor labial salivary gland focus score in Sjögren's disease using deep learning: a tool for more efficient diagnosis and future tissue biomarker discovery.

Journal of autoimmunity
BACKGROUND: Sjögren's Disease (SjD) is histopathologically characterized by focal sialadenitis in minor labial salivary gland biopsies (mLSGB), which is evaluated by utilizing the focus score (FS). Focus score ≥1 identification is a critical step of ...