AIMC Topic: Algorithms

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A Multi-Task Deep Feature Selection Method for Brain Imaging Genetics.

IEEE/ACM transactions on computational biology and bioinformatics
Using brain imaging quantitative traits (QTs) for identifying genetic risk factors is an important research topic in brain imaging genetics. Many efforts have been made for this task via building linear models between imaging QTs and genetic factors ...

Development and application of an intelligent pressure injury assessment system using AI image recognition.

Technology and health care : official journal of the European Society for Engineering and Medicine
BackgroundPressure injuries are a significant concern in clinical settings, requiring accurate assessment to prevent complications. Traditional assessment methods are often subjective and time-consuming.ObjectiveThis study aimed to develop and evalua...

A multi-memory-augmented network with a curvy metric method for video anomaly detection.

Neural networks : the official journal of the International Neural Network Society
Anomaly detection task in video mainly refers to identifying anomalous events that do not conform to the learned normal patterns in the inferring phase. However, the Euclidean metric used in the learning and inferring phase by the most of the existin...

Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery
OBJECTIVE: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of ...

Transferable automatic hematological cell classification: Overcoming data limitations with self-supervised learning.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Classification of peripheral blood and bone marrow cells is critical in the diagnosis and monitoring of hematological disorders. The development of robust and reliable automatic classification systems is hampered by data sca...

MP-FocalUNet: Multiscale parallel focal self-attention U-Net for medical image segmentation.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Medical image segmentation has been significantly improved in recent years with the progress of Convolutional Neural Networks (CNNs). Due to the inherent limitations of convolutional operations, CNNs perform poorly in learni...

OperaGAN: A simultaneous transfer network for opera makeup and complex headwear.

Neural networks : the official journal of the International Neural Network Society
Standard makeup transfer techniques mainly focus on facial makeup. The texture details of headwear in style examples tend to be ignored. When dealing with complex portrait style transfer, simultaneous correct headwear and facial makeup transfer often...

Evaluation of the clinical utility of lateral cephalometry reconstructed from computed tomography extracted by artificial intelligence.

Journal of cranio-maxillo-facial surgery : official publication of the European Association for Cranio-Maxillo-Facial Surgery
This study assessed the accuracy and reliability of artificial intelligence (AI)-reconstructed images of two-dimensional (2D) lateral cephalometric analyses of facial computed tomography (CT) images, which is widely used for the diagnosis of craniofa...

A Graph-Based Machine-Learning Approach Combined with Optical Measurements to Understand Beating Dynamics of Cardiomyocytes.

Journal of computational biology : a journal of computational molecular cell biology
The development of computational models for the prediction of cardiac cellular dynamics remains a challenge due to the lack of first-principled mathematical models. We develop a novel machine-learning approach hybridizing physics simulation and graph...

Comprehensive Investigation of Machine Learning and Deep Learning Networks for Identifying Multispecies Tomato Insect Images.

Sensors (Basel, Switzerland)
Deep learning applications in agriculture are advancing rapidly, leveraging data-driven learning models to enhance crop yield and nutrition. Tomato (), a vegetable crop, frequently suffers from pest damage and drought, leading to reduced yields and f...