Obálka

Advances in Medical Image Processing, Segmentation and Classification

Medical data typically include physiological signals, diagnostic images, and treatment histories, offering essential insights into patient conditions and outcomes. Computer-aided diagnosis (CAD) systems—used for detection, segmentation, and classification—are now key components of clinical workflows...

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Uloženo v:
Médium: Online
Jazyk:angličtina
Vydáno: MDPI - Multidisciplinary Digital Publishing Institute 2025
Témata:
medical image/bio-signal analysis
medical image segmentation/detection
healthcare systems
AI-based medical image registration
medical image recognition
biomedical systems
diagnostic aid
AI-based screening system
medical image
signal classification
biomedical image retrieval
medical image annotation
biomedical image summarization/filtering
cancer diagnosis
machine learning
deep learning
artificial intelligence
AI-based medical image diagnosis
medical deep learning CAD systems
XAI-based medical imaging
patient/treatment stratification based on AI image processing
synthetic medical image generation
explainable AI in medicine
thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine
On-line přístup:ONIX_20250812T110751_9783725841233_339
Tagy: Přidat tag
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Shrnutí:Medical data typically include physiological signals, diagnostic images, and treatment histories, offering essential insights into patient conditions and outcomes. Computer-aided diagnosis (CAD) systems—used for detection, segmentation, and classification—are now key components of clinical workflows. These systems apply image processing techniques to ensure accurate analysis across CT, MRI, X-ray, and ultrasound scans. Artificial intelligence (AI), especially machine learning and deep learning, has further advanced CAD by enabling automated, accurate disease detection. Yet, the success of such models depends on large, annotated datasets and expertise in preprocessing, modeling, and validation. AI-driven CAD systems have shown strong potential in diverse clinical settings. Future work should prioritize multi-center data sharing, federated learning, few-shot learning, and explainable AI to enhance reliability and adaptability. Integrating AI with technologies like the Internet of Medical Things (IoMT) opens doors to real-time, scalable diagnostics. With continued innovation and rigorous validation, AI is set to become an essential part of clinical decision-making. This volume presents cutting-edge research and strategies to address current gaps, aiming to improve patient outcomes and advance global healthcare systems.

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