Stain Color Normalization and Segmentation of Plasma Cells in Microscopic Images as a Prelude to Development of Computer Assisted Automated Disease Diagnostic Tool in Multiple Myeloma
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The ISBI 2019 dataset used contains 10,661 cell images from 73 subjects, including 7,272 leukemic B-lymphoblast (cancer) cell images from 47 patients and 3,389 normal B-lymphoid precursor cell images from 26 healthy persons.
“The data set used to build the diagnostic model is the ISBI 2019 data set [16–20], and 10661 cell pictures of 73 subjects were selected, including 7272 pictures of leukemic B-lymphoblast cells (cancer cells) from 47 all patients and 3389 pictures of B-lymphoid precursors (normal cells) from 26 healthy persons.”
From Method for diagnosis of acute lymphoblastic leukemia based on vit-cnn ensemble model (2021) · cites this paper as [16]
Cited by
Sorted by citations
Sahlol, A. T., Kollmannsberger, P., & Ewees, A. A. (2020) · Scientific Reports · doi:10.1038/s41598-020-59215-9 · Cited by 277 · see citing works
“To overcome the limitation of using a single dataset and to broaden the scope of our work, we extended our study to a second, independent and more recent dataset, C-NMC 30–32 .”
MethodInfluentialAutomated blast cell detection for Acute Lymphoblastic Leukemia diagnosis
Khandekar, R., Shastry, P., Jaishankar, S., Faust, O., & Sampathila, N. (2021) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2021.102690 · Cited by 131 · see citing works
Method for diagnosis of acute lymphoblastic leukemia based on vit-cnn ensemble model
Jiang, Z., Dong, Z., Wang, L., & Jiang, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/7529893 · Cited by 124 · see citing works
“data set used to build the diagnostic model is the ISBI 2019 data set [16–20], and 10661 cell pictures of 73 subjects were selected, including 7272 pictures of leukemic B-lymphoblast cells (cancer cells) from 47 all patients and 3389 pictures of B-lymphoid precursors (normal cells) from 26 healthy…”
MethodHybrid Inception v3 XGBoost Model for Acute Lymphoblastic Leukemia Classification
Ramaneswaran, S., Srinivasan, K., Vincent, P. M. D. R., et al. (2021) · Computational and Mathematical Methods in Medicine · doi:10.1155/2021/2577375 · Cited by 116 · see citing works
Da, Q., Huang, X., Li, Z., Zuo, Y., Zhang, C., Liu, J., Chen, W., Li, J., Xu, D., Hu, Z., et al. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102485 · Cited by 98 · see citing works
An Attention-Based Convolutional Neural Network for Acute Lymphoblastic Leukemia Classification
Ullah, M. Z., Zheng, Y., Song, J., et al. (2021) · Applied Sciences · doi:10.3390/app112210662 · Cited by 77 · see citing works
“Errors in lighting, uneven staining, and image noise have been resolved using the methods given by [11,12].”
MethodAn Aggregated-Based Deep Learning Method for Leukemic B-lymphoblast Classification
Kasani, P. H., Park, S.-W., & Jang, J.-W. (2020) · Diagnostics · doi:10.3390/diagnostics10121064 · Cited by 76 · see citing works
“Illumination errors, inconsistent staining, and noises of the images were fixed with the methods provided by [11,12].”
MethodA Hybrid Deep Learning Architecture for Leukemic B-lymphoblast Classification
Kassani, S. H., Kassani, P. H., Wesolowski, M. J., Schneider, K. A., & Deters, R. (2019) · International Conference on Information and Communication Technology Convergence (ICTC) · doi:10.1109/ictc46691.2019.8939959 · Cited by 72 · see citing works
SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis
Gehlot, S., Gupta, A., & Gupta, R. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101661 · Cited by 65 · see citing works
“msa stain for better visibility of B-type immature white blood cells (WBCs) under the microscope. The images are first normalized for stain color variability across images using the in-house pipeline (Gupta et al., 2017, 2018). Next, cells of interest are marked by expert oncologists. These labeled cells are segmented…”
MethodSelf-citationAcute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning
Liu, Y., & Long, F. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_12 · Cited by 85 · see citing works
Prellberg, J., & Krämer, O. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_6 · Cited by 58 · see citing works
“Additionally, all images have been preprocessed with a stain-normalization procedure that performs white-balancing and fixes errors introduced due to variations in the staining chemical [6].”
MethodA Systematic Collection of Medical Image Datasets for Deep Learning
Li, J., Zhu, G., Hua, C., Feng, M., Bennamoun, B., Li, P., Lu, X., Song, J., Shen, P., Xu, X., et al. (2023) · ACM Computing Surveys · doi:10.1145/3615862 · Cited by 72 · see citing works
“273 SN-AM Dataset [63, 64, 81] 2019 Stain normalization ✓4 Jenner-Giemsa 190 T”
Supervised Learning-based Cancer Detection
Sikder, J., Das, U. K., & Chakma, R. J. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.01205101 · Cited by 45 · see citing works
“For leukemia, the authors used SN-AM Dataset [16][17][18][19][20], which has three subsets.”
MethodGupta, A., Mallick, P., Sharma, O., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0207908 · Cited by 36 · see citing works
Self-citationMachine Learning based System for Automatic Detection of Leukemia Cancer Cell
Mandal, S., Daivajna, V., & Rajagopalan, V. (2019) · IEEE India Conference · doi:10.1109/indicon47234.2019.9029034 · Cited by 35 · see citing works
Deep Learning Based Approach for Multiple Myeloma Detection
Vyshnav, M. T., Sowmya, V., Gopalakrishnan, E. A., Variyar, V. V. S., Menon, V., & K, P. S. (2020) · International Conference on Computing, Communication and Networking Technologies (ICCCNT) · doi:10.1109/icccnt49239.2020.9225651 · Cited by 32 · see citing works
“Meanwhile [14], addresses color normalization of stains and cell segmentation for microscopic images and was an initial step for the development of an automatic plasma cell segmentation tool – PCSeg for microscopic images [15].”
Methodi-Net: a deep CNN model for white blood cancer segmentation and classification
(2022) · International Journal of Advanced Technology and Engineering Exploration · doi:10.19101/ijatee.2021.875564 · Cited by 28 · see citing works
Shah, S., Nawaz, W., Jalil, B., et al. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_3 · Cited by 27 · see citing works
Marzahl, C., Aubreville, M., Voigt, J., et al. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_2 · Cited by 25 · see citing works
Deep Learning for Classifying of White Blood Cancer
Ding, Y., Yang, Y., & Cui, Y. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_4 · Cited by 24 · see citing works
Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images
Honnalgere, A., & Nayak, G. K. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_1 · Cited by 22 · see citing works
Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells
Pan, Y., Liu, M., Xia, Y., & Shen, D. G. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_8 · Cited by 28 · see citing works
“Then, the FV of X is defined as the Fisher information normalized gradient of the log likelihood [7] of X with respect to each mean μ l and standard deviation σ l , shown as follows: where and is the posterior probability of x n being assigned to the l th Gaussian component u l .”
BackgroundMethodInfluentialAcute lymphoblastic leukemia cells image analysis with deep bagging ensemble learningPreprint
Liu, Y., & Long, F. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/580852 · Cited by 19 · see citing works
Cancer Cell Segmentation Based on Unsupervised Clustering and Deep Learning
Sikder, J., Das, U. K., & Anwar, A. M. S. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-68154-8_53 · Cited by 18 · see citing works
An overview of explainable and interpretable AI
Lamberti, W. F. (2023) · Elsevier eBooks · doi:10.1016/b978-0-32-391919-7.00015-9 · Cited by 17 · see citing works
ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification
Verma, E., & Singh, V. P. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_14 · Cited by 16 · see citing works
Intelligent Cancer Detection System
Das, U. K., Sikder, J., Salma, U., et al. (2021) · International Conference on Intelligent Technologies (CONIT) · doi:10.1109/conit51480.2021.9498410 · Cited by 14 · see citing works
Toward Automated Classification of B-Acute Lymphoblastic Leukemia
Kulhalli, R., Savadikar, C., & Garware, B. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_7 · Cited by 20 · see citing works
Mixup Multi-Attention Multi-Tasking Model for Early-Stage Leukemia Identification
Mathur, P., Piplani, M., Sawhney, R., et al. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054672 · Cited by 17 · see citing works
“The dataset images contain staining and illumination errors obtained during clinical processing which were corrected through stain normalization [11].”
MethodLiu, Y., Chen, P., Zhang, J., et al. (2021) · Diagnostics · doi:10.3390/diagnostics12010016 · Cited by 13 · see citing works
“C-NMC is the dataset released by ISBI in 2019 [12], and PD is the clinical dataset collected by our team (All input data are freely available from public sources).”
BackgroundMethodDeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification
Xiao, F., Kuang, R., Ou, Z., et al. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_9 · Cited by 12 · see citing works
Ensemble Convolutional Neural Networks for Cell Classification in Microscopic Images
Shi, T. Z., Wu, L., Zhong, C., Wang, R., & Zheng, W. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_5 · Cited by 16 · see citing works
Classification of Cancer Microscopic Images via Convolutional Neural Networks
Khan, M. A., & Choo, J. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_15 · Cited by 11 · see citing works
Multi-streams and Multi-features for Cell Classification
Xie, X., Li, Y., Zhang, M., et al. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_10 · Cited by 9 · see citing works
A systematic collection of medical image datasets for deep learning
Li, J., Zhu, G., Hua, C., Feng, M., BasheerBennamoun, Li, P., Lu, X., Song, J., Shen, P., Xu, X., et al. (2021) · arXiv · doi:10.48550/arxiv.2106.12864 · Cited by 61 · see citing works
“273 SN-AM Dataset [63, 64, 81] 2019 Stain normalization ✓4 Jenner-Giemsa 190 T”
Improving the Performance of Leukemia Detection using Machine Learning Techniques
Balakumar, K., Gokul Anand, T., Naveenkumar, G., et al. (2022) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc54411.2022.9885461 · Cited by 8 · see citing works
Application of Homomorphic Encryption on Neural Network in Prediction of Acute Lymphoid Leukemia
Khilji, I. Q., Saha, K., Amin, J., et al. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0110646 · Cited by 8 · see citing works
Noshad, A., & Fallahi, S. (2022) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2022.2157748 · Cited by 7 · see citing works
“The third collection is ISBI-2019 C-NMC (Duggal et al. 2016, 2017; Gupta et al. 2017).”
BackgroundLeukemia Cancer Detection Using Image Analytics : (Comparative Study)
Belhekar, A., Gagare, K., Bedse, R., et al. (2019) · International Conference on Computing Communication Control and automation · doi:10.1109/iccubea47591.2019.9128546 · Cited by 7 · see citing works
“Leukemia sample images from C-NMC dataset are shown in the Figure below [5][6][7][8][9].”
MethodHeterogeneity Loss to Handle Intersubject and Intrasubject Variability in CancerPreprint
Goswami, S., Mehta, S., Sahrawat, D., Gupta, A., & Gupta, R. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.03295 · Cited by 17 · see citing works
Self-citationImage Analysis of Cells Acute Lymphoblastic Leukemia Using Ensemble Learning of Deep Bagging
Ullah, A. (2021) · IEEE International Conference on Computing, Engineering and Design (ICCED) · doi:10.1109/icced53389.2021.9664867 · Cited by 5 · see citing works
Aziz, M. T., Mahmud, S., Goh, M. K. O., & Nandi, D. (2024) · Egyptian Informatics Journal · doi:10.1016/j.eij.2024.100479 · Cited by 4 · see citing works
Classification of blood cancer images using a convolutional neural networks ensemble
Ma, K., Sun, L., Wang, Y., et al. (2019) · International Conference on Digital Image Processing · doi:10.1117/12.2539605 · Cited by 3 · see citing works
Lightweight Classification of Normal Versus Leukemic Cells Using Feature Extraction
Anna, Y. F. D. D. S., de Oliveira, J. E. M., & Dantas, D. O. (2021) · Proceedings - IEEE Symposium on Computers and Communications/IEEE Symposium on Computers and Communications · doi:10.1109/iscc53001.2021.9631499 · Cited by 3 · see citing works
Mohajerani, P., & Ntziachristos, V. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_11 · Cited by 2 · see citing works
Leukemic cell detection model based on deep learning
Chen, H. (2020) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1634/1/012046 · Cited by 2 · see citing works
“The data consists of stained images of single cells, including normal white blood cells and leukemic cells [5,6].”
BackgroundInterpretable Lightweight Ensemble Classification of Normal versus Leukemic Cells
de Sant’Anna, Y. F. D., de Oliveira, J. E. M., & Dantas, D. O. (2022) · Computers · doi:10.3390/computers11080125 · Cited by 2 · see citing works
Deep Learning for Medical Image Segmentation: Applications in Disease Detection and Diagnosis
Kannan, R., Jestus, J., & Madesh, P. (2024) · International Conference on Automation, Computing and Renewable Systems (ICACRS) · doi:10.1109/icacrs62842.2024.10841648 · Cited by 1 · see citing works
“Traditional segmentation methods have been unable to effectively manage the complexity and diversity of medical images due to their reliance on human annotations and heuristic-based algorithms [3].”
Leukemic B-Lymphoblast Cell Detection with Monte Carlo Dropout Ensemble Models
Yang, H., & Staib, L. H. (2019) · Lecture notes in bioengineering · doi:10.1007/978-981-15-0798-4_13 · Cited by 3 · see citing works
MİKROSKOBİK GÖRÜNTÜLERDE MULTİPL MİYELOM PLAZMA HÜCRELERİNİN TESPİTİ
GÜNAY, M., & Mehmet, T. A. Z. E. (2022) · Kahramanmaraş Sütçü İmam Üniversitesi Mühendislik Bilimleri Dergisi · doi:10.17780/ksujes.1120829 · Cited by 1 · see citing works
Microscopy Cancer Cell Imaging in B-lineage Acute Lymphoblastic Leukemia
Gupta, A., Gehlot, S., & Gupta, R. (2023) · Elsevier eBooks · doi:10.1016/b978-0-12-819872-8.00008-2 · Cited by 1 · see citing works
Self-citationGeneral stain deconvolution of histopathology images with physics-guided deep learningPreprint
Chen, J., Liu, L., Han, W., Wang, D., Cheung, A., Tsui, H., & Martel, A. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.12.06.519385 · Cited by 1 · see citing works
“As a direct down-stream application of SD, digital pathology images can be normalized to apply an uniform style to images acquired through different scanners and removes a source of variability from down-stream analyses, such as cell segmentation or image classification tasks 25,26 .”
MethodNoshad, A., & fallahi, S. (2021) · Research Square · doi:10.21203/rs.3.rs-575580/v1
RETRACTED: A combined neural network mechanism for categorizing the normal and cancer cells
Vigil, M. S. A., Agarwal, A., Rao, K. B. V. B., et al. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-234188
RetractedNayak, R., Bekal, A., & Saldanha, J. C. (2025) · Yugoslav journal of operations research · doi:10.2298/yjor240315006n
A Transfer Learning CNN Approach for Enhanced Lymphocyte Classification
Gupta, P., & Ahmed, M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-2015-9_23
L-CNN: A Convolutional Neural Network for Leukemia Prediction
Aruta, M., Murano, A., Murano, G. A., et al. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-21376-1_7
- Authors
- Gupta, R., Mallick, P., Duggal, R., Gupta, A., & Sharma, O.
- Affiliations
- All India Institute of Medical Sciences (IN)
- Indraprastha Institute of Information Technology Delhi (IN)
- Venue
- Clinical Lymphoma Myeloma and Leukemia
- Publisher
- Elsevier BV
- Published
- 2017-02
- Volume
- 17
- Issue
- 1
- Pages
- e99
- ISSN
- 2152-2650 (ISSN-L)
- OpenAlex
- W2606483217
- Cited by
- 58
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 5 · 2022: 7 · 2021: 11 · 2020: 9 · 2019: 19 · 2018: 1
- FWCI
- 3.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Clinical Lymphoma Myeloma & Leukemia (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- cell segmentation, plasma cells, multiple myeloma, microscopic images, computer-aided diagnosis, automated disease diagnosis
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