AIMC Topic: Deep Learning

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Non-invasively Discriminating the Pathological Subtypes of Non-small Cell Lung Cancer with Pretreatment F-FDG PET/CT Using Deep Learning.

Academic radiology
RATIONALE AND OBJECTIVES: To develop an end-to-end deep learning (DL) model for non-invasively predicting non-small cell lung cancer (NSCLC) pathological subtypes based on F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (P...

Deep learning for embryo evaluation using time-lapse: a systematic review of diagnostic test accuracy.

American journal of obstetrics and gynecology
OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.

Development and validation of a deep learning model for prediction of intracranial aneurysm rupture risk based on multi-omics factor.

European radiology
OBJECTIVE: The clinical ability of radiomics to predict intracranial aneurysm rupture risk remains unexplored. This study aims to investigate the potential uses of radiomics and explore whether deep learning (DL) algorithms outperform traditional sta...

Differential diagnosis of secondary hypertension based on deep learning.

Artificial intelligence in medicine
Secondary hypertension is associated with higher risks of target organ damage and cardiovascular and cerebrovascular disease events. Early aetiology identification can eliminate aetiologies and control blood pressure. However, inexperienced doctors o...

De novo design of protein interactions with learned surface fingerprints.

Nature
Physical interactions between proteins are essential for most biological processes governing life. However, the molecular determinants of such interactions have been challenging to understand, even as genomic, proteomic and structural data increase. ...

Water quality assessment of deep learning-improved comprehensive pollution index: a case study of Dagu River, Jiaozhou Bay, China.

Environmental science and pollution research international
In the past few decades, with the country's rapid development, water pollution has become a significant problem many countries face. Most of the existing water quality evaluation uses a single time-invariant model to simulate the evolution process, w...

A Data-Efficient Deep Learning Strategy for Tissue Characterization via Quantitative Ultrasound: Zone Training.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Deep learning (DL) powered biomedical ultrasound imaging is an emerging research field where researchers adapt the image analysis capabilities of DL algorithms to biomedical ultrasound imaging settings. A major roadblock to wider adoption of DL power...

The diagnosis of femoroacetabular impingement can be made on pelvis radiographs using deep learning methods.

Joint diseases and related surgery
OBJECTIVES: The aim of this study was to evaluate diagnostic ability of deep learning models, particularly convolutional neural network models used for image classification, for femoroacetabular impingement (FAI) using hip radiographs.

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy.

International journal of molecular sciences
Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions an...