AIMC Topic: Deep Learning

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Deep learning image analysis for continuous single-cell imaging of dynamic processes in Plasmodium falciparum-infected erythrocytes.

Communications biology
Continuous high-resolution imaging of the disease-mediating blood stages of the human malaria parasite Plasmodium falciparum faces challenges due to photosensitivity, small parasite size, and the anisotropy and large refractive index of host erythroc...

Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.

Scientific reports
Applying deep learning algorithms to mine ultrasound features of breast cancer and construct a machine learning model that accurately predicts Ki-67 expression level. This multi-center retrospective study analyzed clinical and ultrasound data from 92...

External validation of artificial intelligence for detection of heart failure with preserved ejection fraction.

Nature communications
Artificial intelligence (AI) models to identify heart failure (HF) with preserved ejection fraction (HFpEF) based on deep-learning of echocardiograms could help address under-recognition in clinical practice, but they require extensive validation, pa...

Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.

Clinical oral investigations
OBJECTIVES: This study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this rela...

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Journal of bioinformatics and computational biology
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Curr...

Optimization of Decision Support Technology for Offshore Oil Condition Monitoring with Carbon Neutrality as the Goal in the Enterprise Development Process.

PloS one
This study aims to explore the integration of the Faster R-CNN (Region-based Convolutional Neural Network) algorithm from deep learning into the MobileNet v2 architecture, within the context of enterprises aiming for carbon neutrality in their develo...

An information-theoretic approach for heterogeneous differentiable causal discovery.

Neural networks : the official journal of the International Neural Network Society
With the advancement of deep learning, a variety of differential causal discovery methods have emerged, inevitably attracting more attention for their excellent scalability and interpretability. However, these methods often struggle with complex hete...

Microscope-Assisted Hypertensive Retinopathy Diagnosis Using Deep Learning Models.

Microscopy research and technique
The retina is the most crucial part of the human eye, and it can be affected due to hypertension. However, retinal abnormalities due to hypertension are termed hypertensive retinopathy (HR). A severe stage of HR can lead to complete blindness if not ...

Synergistic eigenanalysis of covariance and Hessian matrices for enhanced binary classification on health datasets.

Computers in biology and medicine
Covariance and Hessian matrices have been analyzed separately in the literature for classification problems. However, integrating these matrices has the potential to enhance their combined power in improving classification performance. We present a n...

A Deep Retrieval-Enhanced Meta-Learning Framework for Enzyme Optimum pH Prediction.

Journal of chemical information and modeling
The potential of hydrogen (pH) influences the function of the enzyme. Measuring or predicting the optimal pH (pH) at which enzymes exhibit maximal catalytic activity is crucial for enzyme design and application. The rapid development of enzyme mining...