AIMC Topic: Algorithms

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Hierarchical feature extraction on functional brain networks for autism spectrum disorder identification with resting-state fMRI data.

Neural networks : the official journal of the International Neural Network Society
Autism Spectrum Disorder (ASD) is a pervasive developmental disorder of the central nervous system, primarily manifesting in childhood. It is characterized by atypical and repetitive behaviors. Conventional diagnostic methods mainly rely on questionn...

Joint semi-supervised and contrastive learning enables domain generalization and multi-domain segmentation.

Medical image analysis
Despite their effectiveness, current deep learning models face challenges with images coming from different domains with varying appearance and content. We introduce SegCLR, a versatile framework designed to segment images across different domains, e...

A diagnosis and prediction algorithm for juvenile myoclonic epilepsy based on clinical and quantitative EEG features.

Seizure
OBJECTIVE: To develop an objective ensemble machine learning model combining clinical features and quantitative EEG metrics (phase locking value [PLV] and multiscale sample entropy [MSE]) to support accurate diagnosis of juvenile myoclonic epilepsy (...

Integrated multi-omics analysis and machine learning refine molecular subtypes and clinical outcome for hepatocellular carcinoma.

Hereditas
The high morbidity and mortality of hepatocellular carcinoma (HCC) impose a substantial economic burden on patients' families and society, and the majority of HCC patients are detected at advanced stages and experience poor therapeutic outcomes, wher...

Deep learning-based identification of patients at increased risk of cancer using routine laboratory markers.

Scientific reports
Early screening for cancer has proven to improve the survival rate and spare patients from intensive and costly treatments due to late diagnosis. Cancer screening in the healthy population involves an initial risk stratification step to determine the...

Driver facial emotion tracking using an enhanced residual network with weighted fusion of channel and spatial attention.

Scientific reports
Facial expression recognition (FER) plays a crucial role in interpreting human emotions and intentions in real-life applications, such as advanced driver assistance systems. However, it faces challenges due to subtle facial variations, environmental ...

Management of sustainable urban green spaces through machine learning-supported MCDM and GIS integration.

Environmental science and pollution research international
This study evaluates green space suitability in İzmir's Konak district using the analytic hierarchy process, machine learning, weighted linear combination, and the technique for order preference by similarity to ideal solution methods, integrated wit...

Accuracy and Time Efficiency of Automated Tooth Segmentation in Dental Imaging-A Systematic Review and Meta-Analysis.

Orthodontics & craniofacial research
This systematic review examined the accuracy and efficiency of AI-based automated tooth segmentation methods compared to manual or ground truth techniques. A comprehensive search was conducted in MEDLINE (via PubMed), the Cochrane Central Register of...

High-order diversity feature learning for pedestrian attribute recognition.

Neural networks : the official journal of the International Neural Network Society
Pedestrian attribute recognition (PAR) involves accurately identifying multiple attributes present in pedestrian images. There are two main approaches for PAR: part-based method and attention-based method. The former relies on existing segmentation o...