AIMC Topic: Deep Learning

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Neighbor-aware calibration of segmentation networks with penalty-based constraints.

Medical image analysis
Ensuring reliable confidence scores from deep neural networks is of paramount significance in critical decision-making systems, particularly in real-world domains such as healthcare. Recent literature on calibrating deep segmentation networks has res...

Adaptable graph neural networks design to support generalizability for clinical event prediction.

Journal of biomedical informatics
OBJECTIVE: While many machine learning and deep learning-based models for clinical event prediction leverage various data elements from electronic healthcare records such as patient demographics and billing codes, such models face severe challenges w...

Hybrid deep learning downscaling of GCMs for climate impact assessment and future projections in Oman.

Journal of environmental management
Accurate downscaling of global circulation models (GCMs) is critical for assessing the impacts of climate change and water resources management. In this research, Fourteen GCMs were evaluated through a Taylor diagram, including EC-Earth3-CC, ACCESS-C...

Deep learning-driven behavioral analysis reveals adaptive responses in Drosophila offspring after long-term parental microplastic exposure.

Journal of environmental management
Microplastics are widely distributed in the environment and pose potential hazards to organisms. However, our understanding of the transgenerational effects of microplastics on terrestrial organisms remains limited. In this study, we focused on the m...

Machine learning via DARTS-Optimized MobileViT models for pancreatic Cancer diagnosis with graph-based deep learning.

BMC medical informatics and decision making
The diagnosis of pancreatic cancer presents a significant challenge due to the asymptomatic nature of the disease and the fact that it is frequently detected at an advanced stage. This study presents a novel approach combining graph-based data repres...

An explainable and accurate transformer-based deep learning model for wheeze classification utilizing real-world pediatric data.

Scientific reports
Auscultation is a method that involves listening to sounds from the patient's body, mainly using a stethoscope, to diagnose diseases. The stethoscope allows for non-invasive, real-time diagnosis, and it is ideal for diagnosing respiratory diseases an...

Classification patterns identification of immunogenic cell death-related genes in heart failure based on deep learning.

Scientific reports
Heart failure (HF) is a complex and prevalent condition, particularly in the elderly, presenting symptoms like chest tightness, shortness of breath, and dyspnea. The study aimed to improve the classification of HF subtypes and identify potential drug...

Deep learning-based organ-wise dosimetry of Cu-DOTA-rituximab through only one scanning.

Scientific reports
This study aimed to generate a delayed Cu-dotatate (DOTA)-rituximab positron emission tomography (PET) image from its early-scanned image by deep learning to mitigate the inconvenience and cost of estimating absorbed radiopharmaceutical doses. We acq...

Deep learning for detecting and early predicting chronic obstructive pulmonary disease from spirogram time series.

NPJ systems biology and applications
Chronic Obstructive Pulmonary Disease (COPD) is a chronic lung condition characterized by airflow obstruction. Current diagnostic methods primarily rely on identifying prominent features in spirometry (Volume-Flow time series) to detect COPD, but the...

Thermo-responsive and phase-separated hydrogels for cardiac arrhythmia diagnosis with deep learning algorithms.

Biosensors & bioelectronics
Adhesive epidermal hydrogel electrodes are essential for achieving robust signal transduction and cardiac arrhythmia diagnosis, but detachment of conventional adhesive dressings easily causes secondary damage to delicate wound tissues due to lack of ...