By Mr Md Mamunur Rahaman

Book Chapters

, 2023, 'Low Carbon Water Treatment and Energy Recovery', in Zhao X; Dong L; Wang Z (ed.), , MDPI, http://dx.doi.org/10.3390/books978-3-0365-9267-1

Journal articles

Yu Y; Li X; Du T; Rahaman M; Grzegorzek MJ; Li C; Sun H, 2024, 'Increasing the accuracy and reproducibility of positron emission tomography radiomics for predicting pelvic lymph node metastasis in patients with cervical cancer using 3D local binary pattern-based texture features', Intelligent Medicine, 4, pp. 153 - 160, http://dx.doi.org/10.1016/j.imed.2024.03.001

Chen A; Li C; Rahaman MM; Yao Y; Chen H; Yang H; Zhao P; Hu W; Liu W; Zou S; Xu N; Grzegorzek M, 2024, 'Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer: a comprehensive comparative study', Intelligent Medicine, 4, pp. 114 - 127, http://dx.doi.org/10.1016/j.imed.2023.04.001

Chen H; Li X; Li C; Rahaman MM; Li X; Wu J; Sun H; Grzegorzek M; Li X, 2024, 'What can machine vision do for lymphatic histopathology image analysis: a comprehensive review', Artificial Intelligence Review, 57, http://dx.doi.org/10.1007/s10462-024-10701-w

Li R; Li X; Sun H; Yang J; Rahaman M; Grzegozek M; Jiang T; Huang X; Li C, 2024, 'Few-shot learning based histopathological image classification of colorectal cancer', Intelligent Medicine, http://dx.doi.org/10.1016/j.imed.2024.05.003

Rahaman MM; Millar EKA; Meijering E, 2024, 'Generalized deep learning for histopathology image classification using supervised contrastive learning', Journal of Advanced Research, http://dx.doi.org/10.1016/j.jare.2024.11.013

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, pp. 13604, http://dx.doi.org/10.1038/s41598-023-40219-0

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Conference Papers

Rahaman MM; Millar EKA; Meijering E, 2024, 'Histopathology Image Classification Using Supervised Contrastive Deep Learning', in Proceedings - International Symposium on Biomedical Imaging, http://dx.doi.org/10.1109/ISBI56570.2024.10635260

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

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

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

Preprints

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

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

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

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

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

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

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

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

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

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

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

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

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