TIBBIY TASVIRLAR BAZALARI TAHLILI
Kalit so‘zlar:
tibbiy tasvir, sun’iy intellekt, qayta ishlash, tashxislash jarayoni, segmentatsiya, obyekt.Annotatsiya
Mazkur maqolada tibbiy tasvirlar bazalarini xususiyatlari, tarkibi va ularni
sun’iy intellekt algoritmlarini ishlab chiqishdagi ahamiyati tahlil qilingan. Tibbiy tasvirlarni
qayta ishlash sohasidagi NIH ChestX-ray14, LUNA16, BraTS, DRIVE, BUSI kabi keng tarqalgan
ma’lumotlar bazalarini taqqosiy tahlili keltirilgan. Tadqiqotlar natijasida ushbu bazalarni tanib
olish algoritmlarini ishlab chiqish, kasalliklarni erta aniqlash jarayonlaridagi ahamiyati
keltirilgan.
Manbalar
Wang X., Peng Y., Lu L., Lu Z., Bagheri M., Summers R.M. ChestX-ray8: Hospital-Scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases // Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). – 2017. – P. 3462–3471.
Setio A.A.A., Traverso A., de Bel T. et al. Validation, Comparison, and Combination of Algorithms for Automatic Detection of Pulmonary Nodules in Computed Tomography Images: The LUNA16 Challenge // Medical Image Analysis. – 2017. – Vol. 42. – P. 1–13.
Menze B.H., Jakab A., Bauer S. et al. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) // IEEE Transactions on Medical Imaging. – 2015. – Vol. 34, No. 10. – P. 1993–2024.
Staal J., Abramoff M.D., Niemeijer M., Viergever M.A., van Ginneken B. Ridge-Based Vessel Segmentation in Color Images of the Retina // IEEE Transactions on Medical Imaging. – 2004. – Vol. 23, No. 4. – P. 501–509.
Al-Dhabyani W., Gomaa M., Khaled H., Fahmy A. Dataset of Breast Ultrasound Images // Data in Brief. – 2020. – Vol. 28. – Article 104863. – DOI: 10.1016/j.dib.2019.104863.
Litjens G., Kooi T., Bejnordi B.E. et al. A Survey on Deep Learning in Medical Image Analysis // Medical Image Analysis. – 2017. – Vol. 42. – P. 60–88.