Mr Md Mamunur Rahaman

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Md Mamunur Rahaman is currently pursuing his PhD in the School of Computer Science and Engineering at the University of New South Wales (UNSW), Sydney. His research focuses on the development of novel image processing methods, algorithms, and software systems for computer-assisted diagnostics, with a specific focus on medical imaging and microscopic images. He is interested in using sophisticated computational algorithms to extract information and aid in medical intervention.

In 2017, he graduated with a Bachelor of Science in Electrical and Electronics Engineering from BRAC University in Dhaka, Bangladesh. In 2021, he received a Master of Engineering in Biomedical Engineering from Northeastern University in China. At Northeastern University, Mamunur served as a research assistant (RA) from 2021 to January 2022, contributing to the Microscopic Image and Medical Image Analysis Group in the College of Medicine and Bioinformatics Engineering.

In addition to his doctoral research, Mamunur serves as a tutor for various courses at UNSW, including computer vision (COMP9517), neural networks (COMP9444), and artificial intelligence (COMP3411).

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+610405357845

E-mail

md_mamunur.rahaman@unsw.edu.au

Journal articles | 2023

Hu W; Li C; Rahaman MM; Chen H; Liu W; Yao Y; Sun H; Grzegorzek M; Li X, 2023, 'EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation', Physica Medica, vol. 107, pp. 102534 - 102534, http://dx.doi.org/10.1016/j.ejmp.2023.102534

Journal articles | 2023

Kulwa F; Li C; Grzegorzek M; Rahaman MM; Shirahama K; Kosov S, 2023, 'Segmentation of weakly visible environmental microorganism images using pair-wise deep learning features', Biomedical Signal Processing and Control, vol. 79, pp. 104168 - 104168, http://dx.doi.org/10.1016/j.bspc.2022.104168

Journal articles | 2023

Ma P; Li C; Rahaman MM; Yao Y; Zhang J; Zou S; Zhao X; Grzegorzek M, 2023, 'A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches', Artificial Intelligence Review, vol. 56, pp. 1627 - 1698, http://dx.doi.org/10.1007/s10462-022-10209-1

Journal articles | 2023

Zhang J; Li C; Rahaman MM; Yao Y; Ma P; Zhang J; Zhao X; Jiang T; Grzegorzek M, 2023, 'A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements', Archives of Computational Methods in Engineering, vol. 30, pp. 639 - 673, http://dx.doi.org/10.1007/s11831-022-09811-x

Journal articles | 2022

Chen A; Li C; Zou S; Rahaman MM; Yao Y; Chen H; Yang H; Zhao P; Hu W; Liu W; Grzegorzek M, 2022, 'SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis', Biocybernetics and Biomedical Engineering, vol. 42, pp. 204 - 214, http://dx.doi.org/10.1016/j.bbe.2021.12.010

Journal articles | 2022

Chen H; Li C; Li X; Rahaman MM; Hu W; Li Y; Liu W; Sun C; Sun H; Huang X; Grzegorzek M, 2022, 'IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach', Computers in Biology and Medicine, vol. 143, http://dx.doi.org/10.1016/j.compbiomed.2022.105265

Journal articles | 2022

Chen H; Li C; Wang G; Li X; Mamunur Rahaman M; Sun H; Hu W; Li Y; Liu W; Sun C; Ai S; Grzegorzek M, 2022, 'GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection', Pattern Recognition, vol. 130, http://dx.doi.org/10.1016/j.patcog.2022.108827

Journal articles | 2022

Hu W; Li C; Li X; Rahaman MM; Ma J; Zhang Y; Chen H; Liu W; Sun C; Yao Y; Sun H; Grzegorzek M, 2022, 'GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer', Computers in Biology and Medicine, vol. 142, http://dx.doi.org/10.1016/j.compbiomed.2021.105207

Journal articles | 2022

Li X; Li C; Rahaman MM; Sun H; Li X; Wu J; Yao Y; Grzegorzek M, 2022, 'A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches', Artificial Intelligence Review, vol. 55, pp. 4809 - 4878, http://dx.doi.org/10.1007/s10462-021-10121-0

Journal articles | 2022

Li Y; Li C; Li X; Wang K; Rahaman MM; Sun C; Chen H; Wu X; Zhang H; Wang Q, 2022, 'A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis', Archives of Computational Methods in Engineering, vol. 29, pp. 609 - 639, http://dx.doi.org/10.1007/s11831-021-09591-w

Journal articles | 2022

Li Y; Wu X; Li C; Li X; Chen H; Sun C; Rahaman MM; Yao Y; Zhang Y; Jiang T, 2022, 'A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification', Applied Intelligence, vol. 52, pp. 9717 - 9738, http://dx.doi.org/10.1007/s10489-021-02886-2

Journal articles | 2022

Liu W; Li C; Rahaman MM; Jiang T; Sun H; Wu X; Hu W; Chen H; Sun C; Yao Y; Grzegorzek M, 2022, 'Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: From convolutional neural networks to visual transformers', Computers in Biology and Medicine, vol. 141, http://dx.doi.org/10.1016/j.compbiomed.2021.105026

Journal articles | 2022

Liu W; Li C; Xu N; Jiang T; Rahaman MM; Sun H; Wu X; Hu W; Chen H; Sun C; Yao Y; Grzegorzek M, 2022, 'CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron', PATTERN RECOGNITION, vol. 130, http://dx.doi.org/10.1016/j.patcog.2020.108829

Journal articles | 2022

Liu W; Li C; Xu N; Jiang T; Rahaman MM; Sun H; Wu X; Hu W; Chen H; Sun C; Yao Y; Grzegorzek M, 2022, 'CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron', Pattern Recognition, vol. 130, http://dx.doi.org/10.1016/j.patcog.2022.108829

Journal articles | 2022

Zhang J; Li C; Rahaman MM; Yao Y; Ma P; Zhang J; Zhao X; Jiang T; Grzegorzek M, 2022, 'A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches', Artificial Intelligence Review, vol. 55, pp. 2875 - 2944, http://dx.doi.org/10.1007/s10462-021-10082-4

Journal articles | 2022

Zhang J; Zhao X; Jiang T; Rahaman MM; Yao Y; Lin YH; Zhang J; Pan A; Grzegorzek M; Li C, 2022, 'An Application of Pixel Interval Down-Sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images', Applied Sciences (Switzerland), vol. 12, http://dx.doi.org/10.3390/app12147314

Journal articles | 2022

Zhao P; Li C; Rahaman MM; Xu H; Ma P; Yang H; Sun H; Jiang T; Xu N; Grzegorzek M, 2022, 'EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation', Frontiers in Microbiology, vol. 13, http://dx.doi.org/10.3389/fmicb.2022.829027

Journal articles | 2022

Zhao P; Li C; Rahaman MM; Xu H; Yang H; Sun H; Jiang T; Grzegorzek M, 2022, 'A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): From Convolutional Neural Networks to Visual Transformers', Frontiers in Microbiology, vol. 13, http://dx.doi.org/10.3389/fmicb.2022.792166

Journal articles | 2021

Ai S; Li C; Li X; Jiang T; Grzegorzek M; Sun C; Rahaman MM; Zhang J; Yao Y; Li H, 2021, 'A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development', BioMed Research International, vol. 2021, http://dx.doi.org/10.1155/2021/6671417

Journal articles | 2021

Li Z; Li C; Yao Y; Zhang J; Rahaman M; Xu H; Kulwa F; Lu B; Zhu X; Jiang T, 2021, 'EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks', PLoS ONE, vol. 16, http://dx.doi.org/10.1371/journal.pone.0250631

Journal articles | 2021

Rahaman MM; Li C; Yao Y; Kulwa F; Wu X; Li X; Wang Q, 2021, 'DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques', Computers in Biology and Medicine, vol. 136, http://dx.doi.org/10.1016/j.compbiomed.2021.104649

Journal articles | 2020

Li X; Li C; Kulwa F; Rahaman MM; Zhao W; Wang X; Xue D; Yao Y; Cheng Y; Li J; Qi S; Jiang T, 2020, 'Foldover Features for Dynamic Object Behaviour Description in Microscopic Videos', IEEE Access, vol. 8, pp. 114519 - 114540, http://dx.doi.org/10.1109/ACCESS.2020.3003993

Journal articles | 2020

Rahaman MM; Li C; Wu X; Yao Y; Hu Z; Jiang T; Li X; Qi S, 2020, 'A survey for cervical cytopathology image analysis using deep learning', IEEE Access, vol. 8, pp. 61687 - 61710, http://dx.doi.org/10.1109/ACCESS.2020.2983186

Journal articles | 2020

Rahaman MM; Li C; Yao Y; Kulwa F; Rahman MA; Wang Q; Qi S; Kong F; Zhu X; Zhao X, 2020, 'Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches', Journal of X-Ray Science and Technology, vol. 28, pp. 821 - 839, http://dx.doi.org/10.3233/XST-200715

Journal articles | 2020

Sun C; Li C; Zhang J; Rahaman MM; Ai S; Chen H; Kulwa F; Li Y; Li X; Jiang T, 2020, 'Gastric histopathology image segmentation using a hierarchical conditional random field', Biocybernetics and Biomedical Engineering, vol. 40, pp. 1535 - 1555, http://dx.doi.org/10.1016/j.bbe.2020.09.008

Journal articles | 2020

Xu H; Li C; Rahaman MM; Yao Y; Li Z; Zhang J; Kulwa F; Zhao X; Qi S; Teng Y, 2020, 'An enhanced framework of generative adversarial networks (EF-GANs) for environmental microorganism image augmentation with limited rotationinvariant training data', IEEE Access, vol. 8, pp. 187455 - 187469, http://dx.doi.org/10.1109/ACCESS.2020.3031059

Journal articles | 2020

Xue D; Zhou X; Li C; Yao Y; Rahaman MM; Zhang J; Chen H; Zhang J; Qi S; Sun H, 2020, 'An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification', IEEE Access, vol. 8, pp. 104603 - 104618, http://dx.doi.org/10.1109/ACCESS.2020.2999816

Journal articles | 2020

Zhou X; Li C; Rahaman MM; Yao Y; Ai S; Sun C; Wang Q; Zhang Y; Li M; Li X; Jiang T; Xue D; Qi S; Teng Y, 2020, 'A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks', IEEE Access, vol. 8, pp. 90931 - 90956, http://dx.doi.org/10.1109/ACCESS.2020.2993788
Preprints | 2023

Chen A; Zhang J; Rahaman MM; Sun H; D. M; Zeng T; Grzegorzek M; Fan F-L; Li C, 2023, ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos, http://dx.doi.org/10.48550/arxiv.2301.06002

Preprints | 2022

Chen H; Li C; Li X; Rahaman MM; Hu W; Li Y; Liu W; Sun C; Sun H; Huang X; Grzegorzek M, 2022, IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach, http://dx.doi.org/10.48550/arxiv.2206.03368

Preprints | 2022

Hu W; Li C; Li X; Rahaman MM; Zhang Y; Chen H; Liu W; Yao Y; Sun H; Xu N; Huang X; Grzegorze M, 2022, EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation, http://dx.doi.org/10.48550/arxiv.2202.08552

Preprints | 2022

Kulwa F; Li C; Grzegorzek M; Rahaman MM; Shirahama K; Kosov S, 2022, Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features, http://dx.doi.org/10.48550/arxiv.2208.14957

Preprints | 2022

Li X; Chen H; Li C; Rahaman MM; Li X; Wu J; Li X; Sun H; Grzegorzek M, 2022, What Can Machine Vision Do for Lymphatic Histopathology Image Analysis: A Comprehensive Review, http://dx.doi.org/10.48550/arxiv.2201.08550

Preprints | 2022

Liu W; Li C; Xu N; Jiang T; Rahaman MM; Sun H; Wu X; Hu W; Chen H; Sun C; Yao Y; Grzegorzek M, 2022, CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron, http://dx.doi.org/10.48550/arxiv.2206.00971

Preprints | 2022

Zhang J; Li C; Rahaman MM; Yao Y; Ma P; Zhang J; Zhao X; Jiang T; Grzegorzek M, 2022, A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements, http://dx.doi.org/10.48550/arxiv.2202.09020

Preprints | 2022

Zhang J; Zhao X; Jiang T; Rahaman MM; Yao Y; Lin Y-H; Zhang J; Pan A; Grzegorzek M; Li C, 2022, An application of Pixel Interval Down-sampling (PID) for dense tiny microorganism counting on environmental microorganism images, http://dx.doi.org/10.48550/arxiv.2204.01341

Preprints | 2021

Hu W; Li C; Li X; Rahaman MM; Ma J; Zhang Y; Chen H; Liu W; Sun C; Yao Y; Sun H; Grzegorzek M, 2021, GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer, http://dx.doi.org/10.48550/arxiv.2106.02473

Preprints | 2021

Li C; Li X; Rahaman M; Li X; Sun H; Zhang H; Zhang Y; Li X; Wu J; Yao Y; Grzegorzek M, 2021, A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches, http://arxiv.org/abs/2102.10553v1

Conference Papers | 2021

Li Y; Wu X; Li C; Sun C; Li X; Rahaman M; Zhang Y, 2021, 'Intelligent Gastric Histopathology Image Classification Using Hierarchical Conditional Random Field based Attention Mechanism', in ACM International Conference Proceeding Series, pp. 330 - 335, http://dx.doi.org/10.1145/3457682.3457733

Preprints | 2021

Li Y; Wu X; Li C; Sun C; Rahaman M; Chen H; Yao Y; Li X; Zhang Y; Jiang T, 2021, A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification, http://arxiv.org/abs/2102.10499v2

Preprints | 2021

Li Z; Li C; Yao Y; Zhang J; Rahaman MM; Xu H; Kulwa F; Lu B; Zhu X; Jiang T, 2021, EMDS-5: Environmental Microorganism Image Dataset Fifth Version for Multiple Image Analysis Tasks, http://dx.doi.org/10.1371/journal.pone.0250631

Preprints | 2021

Ma P; Li C; Rahaman MM; Yao Y; Zhang J; Zou S; Zhao X; Grzegorzek M, 2021, A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches, http://dx.doi.org/10.48550/arxiv.2105.03148

Preprints | 2021

Rahaman MM; Li C; Yao Y; Kulwa F; Wu X; Li X; Wang Q, 2021, DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques, http://dx.doi.org/10.1016/j.compbiomed.2021.104649

Preprints | 2021

Zhang J; Li C; Rahaman MM; Yao Y; Ma P; Zhang J; Zhao X; Jiang T; Grzegorzek M, 2021, A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches, http://arxiv.org/abs/2103.13625v4

Preprints | 2021

Zhao P; Li C; Rahaman MM; Xu H; Ma P; Yang H; Sun H; Jiang T; Xu N; Grzegorzek M, 2021, EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation, http://dx.doi.org/10.48550/arxiv.2112.07111

Preprints | 2021

Zhao P; Li C; Rahaman MM; Xu H; Yang H; Sun H; Jiang T; Grzegorzek M, 2021, A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers, http://dx.doi.org/10.48550/arxiv.2107.07699

Preprints | 2020

Li Y; Li C; Li X; Wang K; Rahaman MM; Sun C; Chen H; Wu X; Zhang H; Wang Q, 2020, A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis, http://dx.doi.org/10.1007/s11831-021-09591-w

Preprints | 2020

Zhou X; Li C; Rahaman MM; Yao Y; Ai S; Sun C; Li X; Wang Q; Jiang T, 2020, A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks, http://dx.doi.org/10.48550/arxiv.2003.12255

Conference Papers | 2019

Rahaman MM; Chowdhury A; Islam M; Rahman MM, 2019, 'CZTS based thin film solar cell: An investigation into the influence of dark current on cell performance', in 2018 Joint 7th International Conference on Informatics, Electronics and Vision and 2nd International Conference on Imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018, pp. 87 - 92, http://dx.doi.org/10.1109/ICIEV.2018.8641013

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