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Research Article

Lipid droplet detection in histopathological images using reinforcement learning

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Article: 2338433 | Received 03 Jan 2024, Accepted 29 Mar 2024, Published online: 08 Apr 2024

References

  • Ai-Thelaya KA, Gilal NU, Alzubaidi M, et al. Applications of discriminative and deep learning feature extraction methods for whole slide image analysis: a survey. J Pathol Inform. 2023;14:100335. doi: 10.1016/j.jpi.2023.100335.
  • Noorbakhsh J, Farahmand S, Pour AF, et al. Deep learning-based cross-classifications reveal conserved spatial behaviors within tumor histological images. Nat Commun. 2020;11(1):6367. doi: 10.1038/s41467-020-20030-5.
  • Cosatto E, Miller M, Graf HP, et al. Grading nuclear pleomorphism on histological micrographs. Proceedings of the 19th International Conference on Pattern Recognition (ICPR); 2008 Dec 8–11; Tampa, FL.
  • Kobayashi T, Otsu N. Color image feature extraction using color index local auto-correlations. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing; 2009 Apr 19–24; Taipei, Taiwan.
  • Spanhol FA, Oliveira LS, Petitjean C, et al. Breast cancer histopathological image classification using convolutional neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN); 2016 Jul 24–29; Vancouver, BC. doi: 10.1109/IJCNN.2016.7727519.
  • Hou L, Samaras D, Kurc TM, et al. Patch-based convolutional neural network for whole slide tissue image classification. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2016 Jun 27–30; Las Vegas, NV. doi: 10.1109/CVPR.2016.266.
  • Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell. 2017;39(12):2481–2495. doi: 10.1109/TPAMI.2016.2644615.
  • de Bel T, Hermsen M, van der Laak J, et al. Automatic segmentation of histopathological slides of renal tissue using deep learning. SPIE Med Imaging. 2018;10581:37. doi: 10.1117/12.2293717.
  • Zhang D, Song Y, Liu S, et al. Nuclei instance segmentation with dual contour-enhanced adversarial network. Proceedings of the IEEE 15th International Symposium on Biomedical Imaging (ISBI). 2018 Apr 4 − 7; Washington, DC. doi: 10.1109/ISBI.2018.8363604.
  • Goodfellow I, Pouget-Abadie J, Mirza M, et al. Generative adversarial nets. Adv Neural Inf Process Syst. 2014;2672–2680. [Database][Mismatch
  • Schlegl T, Seeböck P, Waldstein SM, et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In: Marc Niethammer, Martin Styner, Stephen Aylward, Hongtu Zhu, Ipek Oguz, Pew-Thian Yap, Dinggang Shen, June 25-30, 2017, Appalachian State University, Boon, NC, USA, Springer, Midtown Manhattan, New York City. Proceedings of the International Conference on Information Processing in Medical Imaging (IPMI); 2017. p. 146–157. doi: 10.1007/978-3-319-59050-9_12.
  • Perera P, Patel VM. Learning deep features for one-class classification. IEEE Trans Image Process. 2019;28(11):5450–5463. doi: 10.1109/TIP.2019.2917862.
  • Barata C, Rotemberg V, Codella NCF, et al. A reinforcement learning model for AI-based decision support in skin cancer. Nat Med. 2023;29(8):1941–1946. doi: 10.1038/s41591-023-02475-5.
  • Zheng T, Chen W, Li S, et al. Learning how to detect: a deep reinforcement learning method for whole-slide melanoma histopathology images. Comput Med Imaging Graph. 2023;108:102275. doi: 10.1016/j.compmedimag.2023.102275.
  • Mushtaq AH, Shafqat A, Salah HT, et al. Machine learning applications and challenges in graft-versus-host disease: a scoping review. Curr Opin Oncol. 2023;35(6):594–600. doi: 10.1097/CCO.0000000000000996.
  • Antunes P, Cruz A, Barbosa J, et al. Lipid droplets in cancer: from composition and role to imaging and therapeutics. Molecules. 2022;27(3):991. doi: 10.3390/molecules27030991.
  • Cruz ALS, Barreto EA, Fazolini NPB, et al. Lipid droplets: platforms with multiple functions in cancer hallmarks. Cell Death Dis. 2020;11(2):105. doi: 10.1038/s41419-020-2297-3.
  • Leow WQ, Chan AW, Mendoza PGL, et al. Non-alcoholic fatty liver disease: the pathologist’s perspective. Clin Mol Hepatol. 2023;29(Suppl):S302–S318. doi: 10.3350/cmh.2022.0329.
  • Mejhert N, Gabriel KR, Frendo-Cumbo S, et al. The lipid droplet knowledge portal: a resource for systematic analyses of lipid droplet biology. Dev Cell. 2022;57(3):387.e4–397.e4. doi: 10.1016/j.devcel.2022.01.003.
  • Lee N, Yang H, Yoo H. A surrogate loss function for optimization of Fβ score in binary classification with imbalanced data [Internet]. arXiv; 2021 [cited 2024 Mar 23]. Available from: http://arxiv.org/abs/2104.01459
  • Huang PW, Lee CH. Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans Med Imaging. 2009;28(7):1037–1050. doi: 10.1109/TMI.2009.2012704.
  • Ishikawa M, Murakami Y, Ahi ST, et al. Automatic quantification of morphological features for hepatic trabeculae analysis in stained liver specimens. J Med Imaging. 2016;3(2):027502. doi: 10.1117/1.JMI.3.2.027502.
  • Calderon F, Garnica-Carrillo A, Reyes-Zuñiga C. Binarization of images with variable lighting using adaptive windows. SIViP. 2022;16(7):1905–1912. doi: 10.1007/s11760-022-02150-1.
  • Ranjan R, Avasthi V. Edge detection using guided Sobel image filtering. Wireless Pers Commun. 2023;132(1):651–677. doi: 10.1007/s11277-023-10628-5.