Mr Md Mamunur Rahaman
Casual Academic

Mr Md Mamunur Rahaman

 

Engineering
Computer Science and Engineering

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). 

Mobile
+610405357845
  • Books | 2023
    2023, Low Carbon Water Treatment and Energy Recovery, Zhao X; Dong L; Wang Z, (eds.), MDPI, http://dx.doi.org/10.3390/books978-3-0365-9267-1
  • Journal articles | 2023
    Chen A; Li C; Rahaman MM; Yao Y; Chen H; Yang H; Zhao P; Hu W; Liu W; Zou S; Xu N; Grzegorzek M, 2023, 'A Comprehensive Comparative Study of Deep Learning Methods for Noisy Sperm Image Classification: from Convolutional Neural Network to Visual Transformer', Intelligent Medicine, http://dx.doi.org/10.1016/j.imed.2023.04.001
    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, 107, 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, 79, 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, 56, pp. 1627 - 1698, http://dx.doi.org/10.1007/s10462-022-10209-1
    Journal articles | 2023
    Rahaman MM; Millar EKA; Meijering E, 2023, 'Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning', Scientific Reports, 13, http://dx.doi.org/10.1038/s41598-023-40219-0
    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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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), 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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, 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 | 2023
    Rahaman MM; Millar EKA; Meijering E, 2023, Breast Cancer Histopathology Image based Gene Expression Prediction using Spatial Transcriptomics data and Deep Learning, , http://dx.doi.org/10.1038/s41598-023-40219-0
    Preprints | 2023
    Rahaman MM; Millar EKA; Meijering E, 2023, Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning, , http://dx.doi.org/10.21203/rs.3.rs-2983276/v1
    Conference Papers | 2023
    Zhang J; Zou S; Li C; Yao Y; Rahaman M; Qian W; Sun H; Grzegorzek M; Wang G, 2023, 'TOD-Net: Transformer-Based Neural Network for Tiny Object Detection in Sperm Microscopic Videos', in Proceedings - International Symposium on Biomedical Imaging, http://dx.doi.org/10.1109/ISBI53787.2023.10230550
    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
    Chen H; Li C; Wang G; Li X; Rahaman M; Sun H; Hu W; Li Y; Liu W; Sun C; Ai S; Grzegorzek M, 2021, GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection, , http://dx.doi.org/10.48550/arxiv.2104.14528
    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 | 2018
    Rahaman MM; Chowdhury A; Islam M; Rahman MM, 2018, '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

 

 

 

  • Professional Member, Association for Computing Machinery (ACM), 2021 - Present
  • Editorial Board Member:
    • Frontiers in Imaging, 2022 - Present
    • Frontiers in Artificial Intelligence, 2022 - Present
    • Frontiers in Big Data, 2022 - Present
  • Reviewer:
    • IEEE Transactions on Medical Imaging, 2022 - Present
    • IEEE Journal of Biomedical and Health Informatics, 2022 - Present
    • Biomedical Signal Processing and Control (Elsevier), 2022 - Present
    • Computers in Biology and Medicine (Elsevier), 2022 - Present
    • Informatics in Medicine Unlocked (Elsevier), 2022 - Present
    • Journal of Personalized Medicine (MDPI), 2022 - Present
    • Sensors (MDPI), 2022 - Present
    • Diagnostics (MDPI), 2022 - Present
    • Cancers (MDPI), 2022 - Present
    • BMC Cancer (Springer Nature), 2022 - Present
    • Scientific Reports (Nature), 2021 - Present
    • World Journal of Surgical Oncology (Springer Nature), 2021 - Present
    • Applied Artificial Intelligence (Taylor & Francis), 2021 - Present
    • BMC Medical Imaging (Springer Nature), 2021 - Present
    • IEEE Access, 2020 - Present

My Research Supervision

 

My Teaching

I have been actively involved in tutoring and mentoring students across several key courses within the School of Computer Science and Engineering. My teaching philosophy is centered on fostering a deep understanding of core concepts, encouraging practical application, and inspiring innovation among students.

Courses Tutored:

  1. Computer Vision (COMP9517)

  2. Neural Networks and Deep Learning (COMP9444)

  3. Artificial Intelligence (COMP3411)