AIMC Topic: Deep Learning

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RAE-Net: a multi-modal neural network based on feature fusion and evidential deep learning algorithm in predicting breast cancer subtypes on DCE-MRI.

Biomedical physics & engineering express
Accurate identification of molecular subtypes in breast cancer is critical for personalized treatment. This study introduces a novel neural network model, RAE-Net, based on Multimodal Feature Fusion (MFF) and the Evidential Deep Learning Algorithm (E...

Chinese medical named entity recognition utilizing entity association and gate context awareness.

PloS one
Recognizing medical named entities is a crucial aspect of applying deep learning in the medical domain. Automated methods for identifying specific entities from medical literature or other texts can enhance the efficiency and accuracy of information ...

Deep-DPC: Deep learning-assisted label-free temporal imaging discovery of anti-fibrotic compounds by controlling cell morphology.

Journal of advanced research
INTRODUCTION: Fibrosis can damage the normal function of many organs, such as cardiac function, for which no effective clinical therapies exist. However, traditional approaches to anti-fibrosis drug discovery have primarily focused on the final biolo...

A PET/CT-based 3D deep learning model for predicting spread through air spaces in stage I lung adenocarcinoma.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE: This study evaluates a three-dimensional (3D) deep learning (DL) model based on fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) for predicting the preoperative status of spread through air spa...

DeepTree-AAPred: Binary tree-based deep learning model for anti-angiogenic peptides prediction.

Journal of molecular graphics & modelling
Anti-angiogenic peptides (AAPs) show important potential in tumor therapy by limiting the growth and metastasis of tumor cells. Accurate prediction of AAPs is of very positive significance for the therapeutic efficacy of tumors. The high cost of wet ...

Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.

Academic radiology
RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-...

Recent advances in AI-driven protein-ligand interaction predictions.

Current opinion in structural biology
Structure-based drug discovery is a fundamental approach in modern drug development, leveraging computational models to predict protein-ligand interactions. AI-driven methodologies are significantly improving key aspects of the field, including ligan...

MSTNet: Multi-scale spatial-aware transformer with multi-instance learning for diabetic retinopathy classification.

Medical image analysis
Diabetic retinopathy (DR), the leading cause of vision loss among diabetic adults worldwide, underscores the importance of early detection and timely treatment using fundus images to prevent vision loss. However, existing deep learning methods strugg...

Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?

La Radiologia medica
The advances in artificial intelligence (AI) technology in recent years have been remarkable, and the field of radiology is at the forefront of applying and implementing these technologies in daily clinical practice. Radiologists must keep up with th...

Prediction of school PM by an attention-based deep learning approach informed with data from nearby air quality monitoring stations.

Chemosphere
Predicting indoor air pollutants concentrations in schools is essential for ensuring a healthy learning environment. Traditional measurements methods pose challenges in cost, maintenance, and time. This study proposes a new approach using a deep lear...