Zeng Taisheng, Ye Yuguang, Chen Yusi, Zhu Daxin, Huang Yifeng, Huang Ying, Chen Yijie, Shi Jianshe, Ding Bijiao, Huang Jianlong, Ling Mengde
Faculty of Mathematics and Computer Science, Quanzhou Normal University, Quanzhou 362000, China.
Fujian Provincial Key Laboratory of Data Intensive Computing, Quanzhou 362000, China.
J Bone Oncol. 2024 Nov 6;49:100645. doi: 10.1016/j.jbo.2024.100645. eCollection 2024 Dec.
Due to its intricate nature and substantial data size, microscopic image data of osteosarcoma often present a significant obstacle to the effectiveness of conventional image retrieval methods. Therefore, this study investigates a new approach for medical image retrieval using advanced deep hashing techniques and attention mechanisms to address these challenges more effectively.
The proposed algorithm significantly improves osteosarcoma cell microscopic image retrieval efficiency and accuracy using deep hashing and attention mechanisms. Image preprocessing includes adaptive histogram equalization and dataset augmentation to enhance quality and diversity. Feature extraction employs the WRN-AM model to map high-dimensional features to a low-dimensional hash code space, improving retrieval efficiency. Finally, similarity matching via Hamming distance allows rapid and precise identification of similar images.
The study shows notable advancements: the WRN-AM model achieves 93.2% classification accuracy and 97.09% mAP using 64-bit hash codes. These findings underscore the technique's effective performance in extracting and categorizing diverse microscopic cell data efficiently and reliably.
This innovative approach provides a robust solution for retrieving and classifying microscopic data of osteosarcoma cells and other cell types, speeding up clinical diagnosis and medical research. It facilitates quicker access and analysis of patient image data, enhancing diagnostic precision and treatment planning for healthcare professionals. Concurrently, it supports researchers in leveraging medical image data more efficiently, fostering progress and innovation in the medical field.
骨肉瘤的微观图像数据因其复杂的性质和庞大的数据量，常常给传统图像检索方法的有效性带来重大障碍。因此，本研究探索一种使用先进的深度哈希技术和注意力机制的医学图像检索新方法，以更有效地应对这些挑战。
所提出的算法利用深度哈希和注意力机制显著提高了骨肉瘤细胞微观图像检索的效率和准确性。图像预处理包括自适应直方图均衡化和数据集增强，以提高质量和多样性。特征提取采用WRN-AM模型将高维特征映射到低维哈希码空间，提高检索效率。最后，通过汉明距离进行相似性匹配，能够快速准确地识别相似图像。
该研究显示出显著进展：WRN-AM模型使用64位哈希码时分类准确率达到93.2%，平均精度均值（mAP）达到97.09%。这些结果强调了该技术在有效且可靠地提取和分类各种微观细胞数据方面的有效性能。
这种创新方法为骨肉瘤细胞及其他细胞类型的微观数据检索和分类提供了强大的解决方案，加快了临床诊断和医学研究的速度。它有助于更快地获取和分析患者图像数据，提高医疗专业人员的诊断精度和治疗规划水平。同时，它支持研究人员更有效地利用医学图像数据，促进医学领域的进步和创新。
