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Biotechnology & Biotechnological Equipment Volume 34, 2020 - Issue 1
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Research Article

A blood cell dataset for lymphoma classification using faster R-CNN

Biaosheng Shenga Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
,
Mei Zhoua Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
,
Menghan Hua Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
,
Qingli Lia Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR China;b Engineering Center of SHMEC for Space Information and GNSS, Shanghai, PR ChinaCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0001-5063-8801View further author information
ORCID Icon,
Li Suna Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
&
Ying Wena Shanghai Key Laboratory of Multidimensional Information Processing, School of Communication and Electronic Engineering, East China Normal University, Shanghai, PR ChinaView further author information
Pages 413-420 | Received 04 Oct 2019, Accepted 04 May 2020, Published online: 13 May 2020

References

  • Siegel RL, Miller KD, Jemal A. Cancer statistics, 2017. CA Cancer J Clin. 2017; 67(1):7–30.
  • Barbe E, de Boer M, de Jong D. A practical cytological approach to the diagnosis of breast-implant associated anaplastic large cell lymphoma. Cytopathology. 2019;30(4):363–369.
  • Willemze R, Cerroni L, Kempf W, et al. The 2018 update of the WHO-EORTC classification for primary cutaneous lymphomas. Blood. 2019;133(16):1703–1714.
  • Garg A, Hosfield EM, Brickner L. Disseminated intravascular large B cell lymphoma with slowly decreasing high-density lipoprotein cholesterol. South Med J. 2011;104(1):53–56.
  • Montes-Mojarro I-A, Bonzheim I, Quintanilla-Martínez L, et al. Molecular diagnosis of lymphoma: a case-based practical approach. Diagn Histopathol. 2019;25(6):229–239.
  • Howell DA, Hart RI, Smith AG, et al. Disease-related factors affecting timely lymphoma diagnosis: a qualitative study exploring patient experiences. Br J Gen Pract. 2019; 69(679) :e134–e145.
  • Robertson S, Azizpour H, Smith K, et al. Digital image analysis in breast pathology-from image processing techniques to artificial intelligence. Transl Res. 2018; 194:19–35.
  • Mohan G, Subashini MM. MRI based medical image analysis: Survey on brain tumor grade classification. Biomed Signal Process Control. 2018; 39:139–161.
  • Razzak MI, Naz S, Zaib A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps. 2018. Cham, Switzerland: Springer. p. 323–350.
  • Habibzadeh M, et al. Automatic white blood cell classification using pre-trained deep learning models: ResNet and Inception. Tenth International Conference on Machine Vision (ICMV 2017), 2018. Bellingham, USA. International Society for Optics and Photonics. 1069612.
  • Tiwari P, Qian J, Li Q, et al. Detection of subtype blood cells using deep learning. Cognit Syst Res. 2018; 52:1036–1044.
  • Sahlol AT, Kollmannsberger P, Ewees AA. Efficient classification of white blood cell leukemia with improved Swarm optimization of deep features. Sci Rep. 2020; 10(1) :2536
  • Rehman A, Abbas N, Saba T, et al. Classification of acute lymphoblastic leukemia using deep learning. Microsc Res Tech. 2018; 81(11) :1310–1317.
  • Ren S, He K, Girshick R, et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks. IEEE Trans Pattern Anal Mach Intell. 2017; 39(6) :1137–1149.
  • Girshick R, et al. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition, Columbus, OH, USA. 2014. 580–587
  • Girshick R. Fast r-cnn. Proceedings of the IEEE international conference on computer vision, Santiago, Chile. 2015, p. 1440–1448.
  • Liu W, et alSsd. Single shot multibox detector. In Leibe B, Matas J, Sebe N, Welling M, editors. European conference on computer vision, Amsterdam, Netherlands. 2016. Springer. 21–37.
  • Tajbakhsh N, et al. Convolutional neural networks for medical image analysis: Full training or fine tuning?. IEEE Trans Med Imag. 2016; 35(5) :1299–1312.
  • Zeiler MD, Fergus R. Visualizing and understanding convolutional networks. in European conference on computer vision. 2014. Springer.818–833
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. 3rd International Conference on Learning Representations, ICLR 2015, 2015. San Diego, CA, United states: International Conference on Learning Representations, ICLR; 2015. 1–14
  • Zorman M, et al. Symbol-based machine learning approach for supervised segmentation of follicular lymphoma images. In Kokol P, Podgorelec V, MiceticTurk D, Zorman M, Verlic M, editors. Twentieth IEEE International Symposium on Computer-Based Medical Systems (CBMS’07). 2007. Los Alamitos, CA, USA. IEEE Computer Soc.115–120.
  • Spinosa EJ, de Leon Ferreira ACP. SVMs for novel class detection in Bioinformatics. WOB, Brasília, Distrito Federal, Brazil. 2004. 2004, p. 81–88.

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