By Dr Mamunur Rahaman

Conference Papers

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, 2025, Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images, http://dx.doi.org/10.48550/arxiv.2503.10731

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/10.48550/arxiv.2303.09987

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

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

Chen H; Li C; Wang G; Li X; 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, http://dx.doi.org/10.48550/arxiv.2104.14528

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

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

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

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 Methods Evaluation, http://dx.doi.org/10.48550/arxiv.2112.07111

Ma P; Li C; Rahaman MM; Yao Y; Zhang J; Zou S; Zhao X; Grzegorzek M, 2022, 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

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

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, http://dx.doi.org/10.48550/arxiv.2107.07699

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

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://dx.doi.org/10.48550/arxiv.2103.13625

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/10.48550/arxiv.2102.12191

Li Y; Li C; Li X; Wang K; Rahaman MM; Sun C; Chen H; Wu X; Zhang H; Wang Q, 2021, A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis, http://dx.doi.org/10.48550/arxiv.2009.13721

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://dx.doi.org/10.48550/arxiv.2102.10553

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, https://arxiv.org/abs/2102.10499v2

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/10.48550/arxiv.2102.10370

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

Rahaman MM; Millar EKA; Meijering E, 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

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