AIMC Topic: Neural Networks, Computer

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ThreeF-Net: Fine-grained feature fusion network for breast ultrasound image segmentation.

Computers in biology and medicine
Convolutional Neural Networks (CNNs) have achieved remarkable success in breast ultrasound image segmentation, but they still face several challenges when dealing with breast lesions. Due to the limitations of CNNs in modeling long-range dependencies...

An Artificial Intelligence Solution for Automated Dental Inspection and Charting.

International dental journal
BACKGROUND: Visual inspection and documentation (charting) of findings are critical components in dental practice. However, manual charting is tedious, time consuming, and prone to errors. Attempts to automate charting using AI have focused on comput...

Accelerating Diffusion: Task-Optimized latent diffusion models for rapid CT denoising.

Computers in biology and medicine
Computed tomography (CT) systems are indispensable for diagnostics but pose risks due to radiation exposure. Low-dose CT (LDCT) mitigates these risks but introduces noise and artifacts that compromise diagnostic accuracy. While deep learning methods,...

Multivariate multi-horizon time-series forecasting for real-time patient monitoring based on cascaded fine tuning of attention-based models.

Computers in biology and medicine
The real-time forecasting of critical physiological indicators in intensive care units (ICUs) is essential for early intervention and clinical decision support. This study introduces a novel framework, StreamHealth Multi-Horizon AI, which has been de...

Fusion of near-infrared and Raman spectroscopy with machine learning strategies: Non-destructive rapid assessment of freshness and TVB-N value prediction in Pacific white shrimp (Litopenaeus vannamei).

Food research international (Ottawa, Ont.)
Total volatile base nitrogen (TVB-N) is a key indicator of shrimp freshness. Nevertheless, traditional detection methods are cumbersome, time-intensive, and destructive. Here, a rapid and non-destructive method based on near-infrared (NIR) and Raman ...

Ensemble learning approach for detecting breast invasive ductal carcinoma from histopathological images.

Pathology, research and practice
Invasive ductal carcinoma is a type of breast cancer that is one of the most frequent and aggressive forms of breast malignancy, necessitating accurate and timely diagnosis for effective treatment. Though considered the gold standard, traditional his...

LiDSCUNet++: A lightweight depth separable convolutional UNet++ for vertebral column segmentation and spondylosis detection.

Research in veterinary science
Accurate computer-aided diagnosis systems rely on precise segmentation of the vertebral column to assist physicians in diagnosing various disorders. However, segmenting spinal disks and bones becomes challenging in the presence of abnormalities and c...

Artificial intelligence (AI)-driven morphological assessment of zebrafish larvae for developmental toxicity chemical screening.

Aquatic toxicology (Amsterdam, Netherlands)
Screening chemicals using the zebrafish embryo developmental toxicity assay requires visual assessment of larval morphological changes based on images by experienced screeners. The process is time-consuming and prone to subjectivity. However, deep le...

HGMSurvNet: A two-stage hypergraph learning network for multimodal cancer survival prediction.

Medical image analysis
Cancer survival prediction based on multimodal data (e.g., pathological slides, clinical records, and genomic profiles) has become increasingly prevalent in recent years. A key challenge of this task is obtaining an effective survival-specific global...

Multi-view hybrid graph convolutional network for volume-to-mesh reconstruction in cardiovascular MRI.

Medical image analysis
Cardiovascular magnetic resonance imaging is emerging as a crucial tool to examine cardiac morphology and function. Essential to this endeavour are anatomical 3D surface and volumetric meshes derived from CMR images, which facilitate computational an...