Segmentation of dermoscopy images based on deformable 3D convolution and ResU-NeXt + + (original) (raw)

References

  1. Schadendorf D, van Akkooi ACJ, Berking C, Griewank KG, Gutzmer R, Hauschild A, Stang A, Roesch A, Ugurel S (2018) Melanoma. Lancet 392:971–984. https://doi.org/10.1016/s0140-6736(18)31559-9
    Article PubMed Google Scholar
  2. Lucas RM, Yazar S, Young AR, Norval M, de Gruijl FR, Takizawa Y, Rhodes LE, Sinclair CA, Neale RE (2019) Human health in relation to exposure to solar ultraviolet radiation under changing stratospheric ozone and climate. Photochem Photobiol Sci 18:641–680. https://doi.org/10.1039/c8pp90060d
    Article PubMed CAS Google Scholar
  3. Hu L, Jin SF, Chen L, Wang YL (2018) Trends in the incidence and mortality of cutaneous melanoma in Hong Kong between 1983 and 2015. Int J Clin Exp Med 11:8259–8266
    Google Scholar
  4. Fong ZV, Tanabe KK (2014) Comparison of melanoma guidelines in the USA, Canada, Europe, Australia and New Zealand: a critical appraisal and comprehensive review. Br J Dermatol 170:20–30. https://doi.org/10.1111/bjd.12687
    Article PubMed CAS Google Scholar
  5. Kasmi R, Mokrani K (2016) Classification of malignant melanoma and benign skin lesions: implementation of automatic ABCD rule. IET Image Process 10:448–455. https://doi.org/10.1049/iet-ipr.2015.0385
    Article Google Scholar
  6. Celebi ME, Kingravi HA, Uddin B, Iyatomi H, Aslandogan YA, Stoecker WV, Moss RH (2007) A methodological approach to the classification of dermoscopy images. Comput Med Imaging Graph 31:362–373. https://doi.org/10.1016/j.compmedimag.2007.01.003
    Article PubMed PubMed Central Google Scholar
  7. Wu J, Chen EZ, Rong R, Li X, Xu D, Jiang H (2019) Skin Lesion Segmentation with C-UNet. In: 2019 41st Annual International Conference of the IEEE. Eng Med Bio Soc:2785–2788. https://doi.org/10.1109/EMBC.2019.8857773
  8. Yang D, Salciccioli J, Marshall D, Sheri A, Shalhoub J (2020) Trends in malignant melanoma mortality in 31 countries from 1985 to 2015. Br J Dermatol 183:1056–1064. https://doi.org/10.1111/bjd.19010
    Article PubMed CAS Google Scholar
  9. Ma L, Shuai R, Ran X, Liu W, Ye C (2020) Combining DC-GAN with ResNet for blood cell image classification. Med Biol Eng Comput 58:1–14. https://doi.org/10.1007/s11517-020-02163-3
    Article Google Scholar
  10. Young K, Booth G, Simpson B, Dutton R, Shrapnel S (2019) Deep neural network or dermatologist? In: Interpretability of machine intelligence in medical image computing and multimodal learning for clinical decision support. Springer, pp 48-55. https://doi.org/10.1007/978-3-030-33850-3_6
  11. Chan S, Reddy V, Myers B, Thibodeaux Q, Brownstone N, Liao W (2020) Machine learning in dermatology: current applications, opportunities, and limitations. Dermatol Ther 10:1–22. https://doi.org/10.1007/s13555-020-00372-0
    Article Google Scholar
  12. Mohammed ZF, Abdulla AA (2021) An efficient CAD system for ALL cell identification from microscopic blood images. Multimed Tools Appl 80:6355–6368
    Article Google Scholar
  13. Mohammed ZF, Abdulla AA (2020) Thresholding-based white blood cells segmentation from microscopic blood images. J Sci Technol 4:9–17
    Google Scholar
  14. Jain S, Pise N (2015) Computer aided melanoma skin cancer detection using image processing. Proc Comput Sci 48:735–740. https://doi.org/10.1016/j.procs.2015.04.209
    Article Google Scholar
  15. Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention:234–241. https://doi.org/10.1007/978-3-319-24574-4_28
  16. Zhang N, Cai Y-X, Wang Y-Y, Tian Y-T, Wang X-L, Badami B (2020) Skin cancer diagnosis based on optimized convolutional neural network. Artif Intell Med 102:101756. https://doi.org/10.1016/j.artmed.2019.101756
    Article PubMed Google Scholar
  17. Dai J, Qi H, Xiong Y, Li Y, Zhang G, Hu H, Wei Y (2017) Deformable convolutional networks. In: Proceedings of the IEEE international conference on computer vision. 2017:7645–773
  18. Oh CM, Cho H, Won YJ, Kong HJ, Roh YH, Jeong KH, Jung KW (2018) Nationwide trends in the incidence of melanoma and non-melanoma skin cancers from 1999 to 2014 in South Korea. Cancer Res Treat 50:729–737. https://doi.org/10.4143/crt.2017.166
    Article PubMed Google Scholar
  19. Gardner LJ, Strunck JL, Wu YP, Grossman D (2019) Current controversies in early-stage melanoma questions on incidence, screening, and histologic regression. J Am Acad Dermatol 80:1–12. https://doi.org/10.1016/j.jaad.2018.03.053
    Article PubMed Google Scholar
  20. Siegel RL, Miller KD, Jemal A (2019) Cancer statistics, 2019. CA Cancer J Clin 69:7–34. https://doi.org/10.3322/caac.21551
    Article PubMed Google Scholar
  21. Feigelson HS, Powers JD, Kumar M, Carroll NM, Pathy A, Ritzwoller DP (2019) Melanoma incidence, recurrence, and mortality in an integrated healthcare system: a retrospective cohort study. Cancer Med 8:4508–4516. https://doi.org/10.1002/cam4.2252
    Article PubMed PubMed Central Google Scholar
  22. Duarte AF, Sousa-Pinto B, Freitas A, Delgado L, Costa-Pereira A, Correia O (2018) Skin cancer healthcare impact: a nation-wide assessment of an administrative database. Cancer Epidemiol 56:154–160. https://doi.org/10.1016/j.canep.2018.08.004
    Article PubMed CAS Google Scholar
  23. Duan Y, Wang J, Hu M, Zhou M, Li Q, Sun L, Qiu S, Wang Y (2019) Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images. Opt Laser Technol 112:530–538. https://doi.org/10.1016/j.optlastec.2018.11.057
    Article CAS Google Scholar
  24. Liu L, Jiang H, He P, Chen W, Liu X, Gao J, Han J (2019) On the variance of the adaptive learning rate and beyond. arXiv. arXiv:190803265
  25. Keskar NS, Socher R (2017) Improving generalization performance by switching from adam to sgd. arXiv. arXiv:171207628
  26. Hua K-L, Hsu C-H, Hidayati SC, Cheng W-H, Chen Y-J (2015) Computer-aided classification of lung nodules on computed tomography images via deep learning technique. OncoTargets Ther:8. https://doi.org/10.2147/OTT.S80733
  27. Mahbod A, Tschandl P, Langs G, Ecker R, Ellinger I (2020) The effects of skin lesion segmentation on the performance of dermatoscopic image classification. Comput Methods Prog Biomed 197:105725
    Article Google Scholar
  28. Ballerini L, Fisher RB, Aldridge B, Rees J (2013) A color and texture based hierarchical K-NN approach to the classification of non-melanoma skin lesions. In: Color medical image analysis. Springer, pp 63-86. https://doi.org/10.1007/978-94-007-5389-1_4
  29. Mishra NK, Celebi ME (2016) An overview of melanoma detection in dermoscopy images using image processing and machine learning. arXiv. arXiv:160107843
  30. Cheng Y, Swamisai R, Umbaugh SE, Moss RH, Stoecker WV, Teegala S, Srinivasan SK (2008) Skin lesion classification using relative color features. Skin Res Technol 14:53–64. https://doi.org/10.1111/j.1600-0846.2007.00261.x
    Article PubMed Google Scholar
  31. Jha D, Smedsrud PH, Riegler MA, Johansen D, De Lange T, Halvorsen P, Johansen HD (2019) ResUNet++: an advanced architecture for medical image segmentation. In: 2019 IEEE International Symposium on Multimedia (ISM), pp 225–2255. https://doi.org/10.1109/ISM46123.2019.00049
  32. Huang X (2017) Belongie S Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision, pp 1501–1510
  33. Jing Y, Liu X, Ding Y, Wang X, Wen S (2020) Dynamic instance normalization for arbitrary style transfer. Proceedings of the AAAI Conference on Artificial Intelligence 34:4369–4376
    Article Google Scholar
  34. Alom MZ, Hasan M, Yakopcic C, Taha TM, Asari VK (2018) Recurrent residual convolutional neural network based on u-net (r2u-net) for medical image segmentation. arXiv:180206955
  35. Zhang L, Yang G, Ye X (2019) Automatic skin lesion segmentation by coupling deep fully convolutional networks and shallow network with textons. J Med Imaging 6:024001. https://doi.org/10.1117/1.JMI.6.2.024001
    Article Google Scholar
  36. Ali A-R, Li J, O’Shea SJ, Yang G, Trappenberg T, Ye X A (2019) Deep learning based approach to skin lesion border extraction with a novel edge detector in dermoscopy images. In: 2019 International Joint Conference on Neural Networks (IJCNN). IEEE, pp 1-7 https://doi.org/10.1109/IJCNN.2019.8852134
  37. Yu L, Chen H, Dou Q, Qin J, Heng P-A (2016) Automated melanoma recognition in dermoscopy images via very deep residual networks. IEEE Trans Med Imaging 36:994–1004. https://doi.org/10.1109/TMI.2016.2642839
    Article PubMed Google Scholar
  38. Zhang Z, Liu Q, Wang Y (2017) Road extraction by deep residual U-Net. IEEE Geoence and. Remote Sens Lett:1–5
  39. He K, Zhang X, Ren S (2016) Sun J Deep residual learning for image recognition. In: IEEE Conference on Computer Vision & Pattern Recognition
  40. Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Springer, pp 3-11. https://doi.org/10.1007/978-3-030-00889-5_1
  41. Xie S, Girshick R, Dollár P, Tu Z, He K (2016) Aggregated residual transformations for deep neural networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016
  42. Jie H, Li S, Samuel A, Gang S, Enhua W (2019) Squeeze-and-excitation networks. IEEE Trans Pattern Anal Mach Intell
  43. Jia X, De Brabandere B, Tuytelaars T, Van Gool L (2016) Dynamic filter networks[J]. Advances in neural information processing systems 29:667–675
  44. Tschandl P, Rosendahl C, Kittler H (2018) The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions. Sci Data 5:180161. https://doi.org/10.1038/sdata.2018.161
    Article PubMed PubMed Central Google Scholar
  45. Codella N, Rotemberg V, Tschandl P, Celebi ME, Dusza S, Gutman D, Helba B, Kalloo A, Liopyris K, Marchetti M (2019) Skin lesion analysis toward melanoma detection 2018: a challenge hosted by the international skin imaging collaboration. arXiv:190203368
  46. Telea A (2004) An image inpainting technique based on the fast marching method. J Graph Tools 9:23–34. https://doi.org/10.1080/10867651.2004.10487596
    Article Google Scholar
  47. Wang G, Wang Y, Li H, Chen X, Lu H, Ma Y, Peng C, Wang Y, Tang L (2014) Morphological background detection and illumination normalization of text image with poor lighting. PLoS ONE 9:e110991. https://doi.org/10.1371/journal.pone.0110991
    Article PubMed PubMed Central CAS Google Scholar
  48. Thapar S, Garg S (2012) Study and implementation of various morphology based image contrast enhancement techniques. CSE Department, IT Department, pp 2–5
    Google Scholar
  49. Arora R, Raman B, Nayyar K, Awasthi R Automated skin lesion segmentation using attention-based deep convolutional neural network. Biomed Signal Process Control 65:102358
  50. Zhu L, Feng S, Zhu W, Chen X (2020) ASNet: An adaptive scale network for skin lesion segmentation in dermoscopy images. In: Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging. International Society for Optics and Photonics 113170W https://doi.org/10.1117/12.2549178
  51. Jafari M, Auer D, Francis S, Garibaldi J, Chen X (2020) DRU-Net: an efficient deep convolutional neural network for medical image segmentation. In: 2020 IEEE 17th International Symposium on Biomedical Imaging, pp 1144-1148. https://doi.org/10.1109/ISBI45749.2020.9098391
  52. Saini S, Gupta D, Tiwari AK (2020) Detector-SegMentor network for skin lesion localization and segmentation. arXiv 200506550
  53. Ibtehaz N, Rahman MS (2020) MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Netw 121:74–87. https://doi.org/10.1016/j.neunet.2019.08.025
    Article PubMed Google Scholar
  54. Al Nazi Z, Abir TA (2020) Automatic skin lesion segmentation and melanoma detection: transfer learning approach with U-Net and DCNN-SVM. In: Proceedings of International Joint Conference on Computational Intelligence. Springer, pp 371-381. https://doi.org/10.1007/978-981-13-7564-4_32
  55. Jha D, Riegler MA, Johansen D, Halvorsen P, Johansen HD (2020) DoubleU-Net: a deep convolutional neural network for medical image segmentation. arXiv 200604868

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