AIMC Topic: Deep Learning

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SE-ATT-YOLO- A deep learning driven ultrasound based respiratory motion compensation system for precision radiotherapy.

Computers in biology and medicine
OBJECTIVE: The therapeutic management of neoplasm employs high level energy beam to ablate malignant cells, which can cause collateral damage to adjacent normal tissue. Furthermore, respiration-induced organ motion, during radiotherapy can lead to si...

Deep learning dosiomics in grade 4 radiation-induced lymphopenia prediction in radiotherapy for esophageal cancer: a multi-center study.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: To investigate the feasibility and accuracy of using deep learning and dosiomics features, as well as their combination with dose-volume histogram (DVH) parameters and clinical factors to predict grade 4 radiation-induced lymphopenia (G4RIL)...

SimTA++: Simple attention neural network for clinical asynchronous time series.

Neural networks : the official journal of the International Neural Network Society
The task of modeling asynchronous time series data in clinical practice is a formidable challenge, made even more difficult by the multi-modal and irregular nature of real-world medical time series data. Despite advances in deep learning for sequenti...

Discovery of milk-derived antimicrobial peptides in human milk by DeepMAMP based on peptidomics technology and deep learning method.

Food chemistry
Milk-derived antimicrobial peptides (MAMPs) in human milk (HMAMPs) play an important role in the nutrition and the immune system construction of newborns. Current AMP prediction models cannot accurately predict HMAMPs, thus high-throughput and target...

PMFF-Net: A deep learning-based image classification model for UIP, NSIP, and OP.

Computers in biology and medicine
BACKGROUND: High-resolution computed tomography (HRCT) is helpful for diagnosing interstitial lung diseases (ILD), but it largely depends on the experience of physicians. Herein, our study aims to develop a deep-learning-based classification model to...

BioTransX: A novel bi-former based hybrid model with bi-level routing attention for brain tumor classification with explainable insights.

Computers in biology and medicine
Brain tumors, known for their life-threatening implications, underscore the urgency of precise and interpretable early detection. Expertise remains essential for accurate identification through MRI scans due to the intricacies involved. However, the ...

Spindle Autoencoder-CNN hybrid model for cardiac arrhythmia classification.

Computers in biology and medicine
Cardiac arrhythmias, characterized by irregular heart function, disrupt normal blood circulation and are commonly detected using electrocardiograms (ECGs). ECG is widely preferred due to its cost-effectiveness, ease of application, and high reliabili...

Generative deep-learning-model based contrast enhancement for digital subtraction angiography using a text-conditioned image-to-image model.

Computers in biology and medicine
BACKGROUND: Digital subtraction angiography (DSA) is an essential imaging technique in interventional radiology, enabling detailed visualization of blood vessels by subtracting pre- and post-contrast images. However, reduced contrast, either accident...

Quantification of Breast Arterial Calcification in Mammograms Using a UNet-Based Deep Learning for Detecting Cardiovascular Disease.

Academic radiology
BACKGROUND: Breast arterial calcification (BAC) is increasingly recognized as a significant indicator of cardiovascular risk, necessitating improvements in detection and quantification methods through mammographic screening.

Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.

American journal of human genetics
Electronic health records, biobanks, and wearable biosensors enable the collection of multiple health modalities from many individuals. Access to multimodal health data provides a unique opportunity for genetic studies of complex traits because diffe...