By Mr Md Mamunur Rahaman

Journal articles

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

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

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

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

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