LR-CSNet: Low-Rank Deep Unfolding Network for Image Compressive Sensing

Zhang, Tianfang; Li, Lei; Igel, Christian; Oehmcke, Stefan; Gieseke, Fabian; Peng, Zhenming

doi:10.1109/iccc56324.2022.10065722

Cited by 8 publications

(1 citation statement)

References 31 publications

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“…Perceptual loss evaluates these differences by extracting output and real image features using a pre-trained image classification network, focusing on high-level attributes like content and texture [41]. Minimizing the disparities between these features, the model learns to transform the input image into an output that mirrors the actual image's high-level characteristics.…”

Section: E Enhancements Through Perceptual Adversarial Network (Pan)mentioning

confidence: 99%

Advancements in AI for Oncology: Developing an Enhanced YOLOv5-based Cancer Cell Detection System

Chen,

Hu,

et al. 2024

IJIRCST

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As artificial intelligence (AI) theory becomes more sophisticated and its utilization spreads across daily life, education, and professional settings, the adoption of AI for medical diagnostic and service purposes stands as a logical progression in the evolution of medical technologies. This document outlines a novel approach to detecting cancer cell targets using a deep learning-based system, marking a critical step towards integrating AI into cancer diagnostics. The process of detecting cancer cell targets entails the localization of cell types within images of cells. By capitalizing on the strengths of the YOLOv5 model—a deep learning-driven, end-to-end, real-time object detection framework known for its efficiency, superior performance, adaptability, and user-friendly PyTorch integration—this research presents an enhanced YOLOv5 model incorporating both a feature pyramid network and the original YOLOv5 architecture. The ultimate aim is to facilitate precise detection of targets in cancer cell images. The experimental data demonstrate the system's negligible error rate in detection, swift processing capabilities, and exceptional reliability.

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Section: E Enhancements Through Perceptual Adversarial Network (Pan)mentioning

confidence: 99%