AIMC Topic: Deep Learning

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Cuff-less blood pressure monitoring via PPG signals using a hybrid CNN-BiLSTM deep learning model with attention mechanism.

Scientific reports
Blood pressure (BP) serves as a fundamental indicator of cardiovascular health, measuring the force exerted by circulating blood against arterial walls during each heartbeat. This paper introduces an advanced deep learning framework for precise, non-...

Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods.

Scientific reports
In recent years, the prevalence of chronic diseases such as Ulcerative Colitis (UC) has increased, bringing a heavy burden to healthcare systems. Traditional Chinese Medicine (TCM) stands out for its cost-effective and efficient treatment modalities,...

Lessons learned from RadiologyNET foundation models for transfer learning in medical radiology.

Scientific reports
Deep learning models require large amounts of annotated data, which are hard to obtain in the medical field, as the annotation process is laborious and depends on expert knowledge. This data scarcity hinders a model's ability to generalise effectivel...

Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution.

Scientific reports
Anterior Cruciate Ligament (ACL) tears are common in sports and can provide noteworthy health issues. Therefore, accurately diagnosing of tears is important for the early and proper treatment. However, traditional diagnostic methods, such as clinical...

Language models learn to represent antigenic properties of human influenza A(H3) virus.

Scientific reports
Given that influenza vaccine effectiveness depends on a good antigenic match between the vaccine and circulating viruses, it is important to assess the antigenic properties of newly emerging variants continuously. With the increasing application of r...

Ultrasound-based classification of follicular thyroid Cancer using deep convolutional neural networks with transfer learning.

Scientific reports
This study aimed to develop and validate convolutional neural network (CNN) models for distinguishing follicular thyroid carcinoma (FTC) from follicular thyroid adenoma (FTA). Additionally, this current study compared the performance of CNN models wi...

Enhancing ultrasonographic detection of hepatocellular carcinoma with artificial intelligence: current applications, challenges and future directions.

BMJ open gastroenterology
BACKGROUND: Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, with early detection playing a crucial role in improving survival rates. Artificial intelligence (AI), particularly in medical image analysis, h...

Enhancing the Predictions of Cytomegalovirus Infection in Severe Ulcerative Colitis Using a Deep Learning Ensemble Model: Development and Validation Study.

JMIR medical informatics
BACKGROUND: Cytomegalovirus (CMV) reactivation in patients with severe ulcerative colitis (UC) leads to worse outcomes; yet, early detection remains challenging due to the reliance on time-intensive biopsy procedures.

Data augmentation of time-series data in human movement biomechanics: A scoping review.

PloS one
BACKGROUND: The integration of machine learning and deep learning methodologies has transformed data analytics in biomechanics. However, the field faces challenges such as limited large-scale data sets, high data acquisition costs, and restricted par...

D2C-Morph: Brain regional segmentation based on unsupervised registration network with similarity analysis.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Brain regional segmentation is an image-processing approach widely used in brain image analyses. Deep learning models that perform segmentation alone play an important role in medical fields such as automatic diagnosis and prognosis prediction. This ...