AIMC Topic: Image Processing, Computer-Assisted

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Performance of two different artificial intelligence models in dental implant planning among four different implant planning software: a comparative study.

BMC oral health
BACKGROUND: The integration of artificial intelligence (AI) in dental implant planning has emerged as a transformative approach to enhance diagnostic accuracy and efficiency. This study aimed to evaluate the performance of two object detection models...

Deep learning-based quantitative analysis of glomerular morphology in IgA nephropathy whole slide images and its prognostic implications.

Scientific reports
Kidney pathology of immunoglobulin A nephropathy (IgAN), which is the key finding of both diagnosis and risk stratification, involves labor-intensive manual interpretation as well as unavoidable interpreter-dependent variabilities. We propose artific...

Optimizing visual data retrieval using deep learning driven CBIR for improved human machine interaction.

Scientific reports
Content-based image retrieval (CBIR) systems have formidable obstacles in connecting human comprehension with machine-driven feature extraction due to the exponential expansion of visual data across many areas. Robust performance across varied datase...

WeedSwin hierarchical vision transformer with SAM-2 for multi-stage weed detection and classification.

Scientific reports
Weed detection and classification using computer vision and deep learning techniques have emerged as crucial tools for precision agriculture, offering automated solutions for sustainable farming practices. This study presents a comprehensive approach...

A federated learning-based privacy-preserving image processing framework for brain tumor detection from CT scans.

Scientific reports
The detection of brain tumors is crucial in medical imaging, because accurate and early diagnosis can have a positive effect on patients. Because traditional deep learning models store all their data together, they raise questions about privacy, comp...

Ensemble methods and partially-supervised learning for accurate and robust automatic murine organ segmentation.

Scientific reports
Delineation of multiple organs in murine µCT images is crucial for preclinical studies but requires manual volumetric segmentation, a tedious and time-consuming process prone to inter-observer variability. Automatic deep learning-based segmentation c...

Deep learning strategies for semantic segmentation of pediatric brain tumors in multiparametric MRI.

Scientific reports
Automated segmentation of pediatric brain tumors (PBTs) can support precise diagnosis and treatment monitoring, but it is still poorly investigated in literature. This study proposes two different Deep Learning approaches for semantic segmentation of...

Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation.

Scientific reports
Fusing multimodal data play a crucial role in accurate brain tumor segmentation network and clinical diagnosis, especially in scenarios with incomplete multimodal data. Existing multimodal fusion models usually perform intra-modal fusion at both shal...

An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning.

Scientific reports
Deep learning primarily operates on images which contain hidden patterns that are quantified through pixel intensities. Deep learning is used to analyze the image patterns and to recognize the objects. The detection process includes the creation of l...

Multi channel fusion diffusion models for brain tumor MRI data augmentation.

Scientific reports
The early diagnosis of brain tumors is crucial for patient prognosis, and medical imaging techniques such as MRI and CT scans are essential tools for diagnosing brain tumors. However, high-quality medical image data for brain tumors is often scarce a...