A Comprehensive Survey of Deep Learning Approaches in Image Processing

Sensors, Год журнала: 2025, Номер 25(2), С. 531 - 531

Опубликована: Янв. 17, 2025

The integration of deep learning (DL) into image processing has driven transformative advancements, enabling capabilities far beyond the reach traditional methodologies. This survey offers an in-depth exploration DL approaches that have redefined processing, tracing their evolution from early innovations to latest state-of-the-art developments. It also analyzes progression architectural designs and paradigms significantly enhanced ability process interpret complex visual data. Key such as techniques improving model efficiency, generalization, robustness, are examined, showcasing DL's address increasingly sophisticated image-processing tasks across diverse domains. Metrics used for rigorous evaluation discussed, underscoring importance performance assessment in varied application contexts. impact is highlighted through its tackle challenges generate actionable insights. Finally, this identifies potential future directions, including emerging technologies like quantum computing neuromorphic architectures efficiency federated privacy-preserving training. Additionally, it highlights combining with edge explainable artificial intelligence (AI) scalability interpretability challenges. These advancements positioned further extend applications DL, driving innovation processing.

Язык: Английский

A Survey on Multimodal Large Language Models in Radiology for Report Generation and Visual Question Answering

Information, Год журнала: 2025, Номер 16(2), С. 136 - 136

Опубликована: Фев. 12, 2025

Large language models (LLMs) and large vision (LVMs) have driven significant advancements in natural processing (NLP) computer (CV), establishing a foundation for multimodal (MLLMs) to integrate diverse data types real-world applications. This survey explores the evolution of MLLMs radiology, focusing on radiology report generation (RRG) visual question answering (RVQA), where leverage combined capabilities LLMs LVMs improve clinical efficiency. We begin by tracing history development MLLMs, followed an overview MLLM applications RRG RVQA, detailing core datasets, evaluation metrics, leading that demonstrate their potential generating reports image-based questions. then discuss challenges face including dataset scarcity, privacy security, issues within such as bias, toxicity, hallucinations, catastrophic forgetting, limitations traditional metrics. Finally, this paper proposes future research directions address these challenges, aiming help AI researchers radiologists overcome obstacles advance study radiology.

Язык: Английский

Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images

Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 132 - 142

Опубликована: Окт. 4, 2024

Язык: Английский