Unpaired Bone Marrow Smears Virtual Staining Using Content and Attention-Guided Generative Adversarial Networks: UBMSVStain Using Content and Attention-Guided Generative Adversarial Networks
Authors: 
Aman Wang, Ruijie ZhuAuthors Info & Claims
Pages 676 - 681
Abstract
Morphological examination of bone marrow cells is the standard for the diagnosis of Acute Lymphoblastic Leukemia (ALL). However, the preparation of bone marrow smears requires tedious staining steps, trained pathologists, and a professional experimental environment. Hence, a method that can transform bright-field microscopy images of unstained bone marrow cell smears into Wright & Giemsa (W&G)-stained images of the same samples is essential. However, paired image data is hardly available. In addition, existing unsupervised methods have limitations on datasets of entirely unpaired and complex texture images. This paper proposes an Unpaired Bone Marrow Smears Virtual Staining (UBMSVStain) method, in which a Content And Attention-Guided Staining (CAGS) module is designed to enhance the features after the skip connections and improve the preservation of structural information. All experimental results show that UBMSVStain not only achieves virtual staining of bone marrow smears but also has superior performance.
References
[1]
Rory M. Shallis, Rong Wang, Amy Davidoff, Xiaomei Ma, and Amer M. Zeidan. 2019. Epidemiology of acute myeloid leukemia: Recent progress and enduring challenges. Blood reviews, 36, 70-87. https://doi.org/10.1016/j.blre.2019.04.005
[2]
Tapas Bhadra, Saurav Mallik, Amir Sohel, and Zhongming Zhao. 2021. Unsupervised Feature Selection Using an Integrated Strategy of Hierarchical Clustering With Singular Value Decomposition: An Integrative Biomarker Discovery Method With Application to Acute Myeloid Leukemia. IEEE/ACM Trans. Comput. Biol. Bioinformatics 19, 3 (May-June 2022), 1354–1364. https://doi.org/10.1109/TCBB.2021.3110989
[3]
Huajian Liu, Martin Steinebach, and Kathrin Schölei. 2019. Improved Manipulation Detection with Convolutional Neural Network for JPEG Images. In Proceedings of the 14th International Conference on Availability, Reliability and Security (ARES '19). Association for Computing Machinery, New York, NY, USA, Article 39, 1–6. https://doi.org/10.1145/3339252.3340526
[4]
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Advances in neural information processing systems, 27.
[5]
Fayaz Ali Dharejo, Farah Deeba, Yuanchun Zhou, Bhagwan Das, Munsif Ali Jatoi, Muhammad Zawish, Yi Du, and Xuezhi Wang. 2021. TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution. ACM Trans. Intell. Syst. Technol. 12, 6, Article 71 (December 2021), 20 pages. https://doi.org/10.1145/3456726
[6]
Roohi Sille, Tanupriya Choudhury, Ashutosh Sharma, Piyush Chauhan, Ravi Tomar, and Durgansh Sharma. 2023. A novel generative adversarial network-based approach for automated brain tumour segmentation. Medicina, 59(1), 119. https://doi.org/10.3390/medicina59010119
[7]
Junyan Zhu, Taesung Park, Phillip Isola, and Alexei A. Efros. 2017. Unpaired image-to-image translation using cycle-consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision, 2223-2232.
[8]
Yuta Hiasa, Yoshito Otake, Masaki Takao, Takumi Matsuoka, Kazuma Takashima, Aaron Carass, Jerry L. Prince, Nobuhiko Sugano, and Yoshinobu Sato. 2018. Cross-modality image synthesis from unpaired data using CycleGAN: Effects of gradient consistency loss and training data size. In Simulation and Synthesis in Medical Imaging: Third International Workshop, SASHIMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 3, 31-41. Springer International Publishing.
[9]
Jiayuan Wang, Q.M. Jonathan Wu, and Farhad Pourpanah. 2023. Dc-cyclegan: bidirectional ct-to-mr synthesis from unpaired data. Computerized Medical Imaging and Graphics, 102249. https://doi.org/10.1016/j.compmedimag.2023.10224
Index Terms
- Unpaired Bone Marrow Smears Virtual Staining Using Content and Attention-Guided Generative Adversarial Networks: UBMSVStain Using Content and Attention-Guided Generative Adversarial Networks
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Published In
October 2023
1394 pages
ISBN:9798400708138
DOI:10.1145/3644116
Copyright © 2023 ACM.
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Association for Computing Machinery
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Published: 05 April 2024
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ISAIMS 2023
ISAIMS 2023: 2023 4th International Symposium on Artificial Intelligence for Medicine Science
October 20 - 22, 2023
Chengdu, China
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Overall Acceptance Rate 53 of 112 submissions, 47%
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