Journal Articles

1. Leveraging Vision-Language Embeddings for Zero-Shot Learning in Histopathology Images. Rahaman MM; Millar EKA; Meijering E (2026). IEEE Journal of Biomedical and Health Informatics, 30, pp. 539–550.
2. PET/CT radiomics for non-invasive prediction of immunotherapy efficacy in cervical cancer. Du T; Li C; Grzegozek M; Huang X; Rahaman M; Wang X; Sun H (2025). Journal of X-Ray Science and Technology, 33, pp. 1081–1092.
3. COVID-19CT+: A public dataset of CT images for COVID-19 retrospective analysis. Sun Y; Du T; Wang B; Rahaman MM; Wang X; Huang X; Jiang T; Grzegorzek M; Sun H; Xu J; Li C (2025). Journal of X-Ray Science and Technology, 33, pp. 901–915.
4. Generalized deep learning for histopathology image classification using supervised contrastive learning. Rahaman MM; Millar EKA; Meijering E (2025). Journal of Advanced Research, 75, pp. 389–404.
5. Channel-Gated Transformers with Affinity CAM for Weakly Supervised Multi-Class Brain Tumor Segmentation. Han Y; Liu K; Yuan L; Rahaman M; Grzegorzek M; Sun H; Li C; Chen H (2025). IEEE Journal of Biomedical and Health Informatics.
6. Dual-Level Imbalance Mitigation for Single-FoV Colorectal Histopathology Image Classification. Yuan L; Chen Y; Rahaman M; Sun H; Chen H; Grzegorzek M; Li C; Li X (2025). IEEE Journal of Biomedical and Health Informatics.
7. Prediction of TP53 mutations across female reproductive system pan-cancers using deep multimodal PET/CT radiogenomics. Du T; Jiang T; Li X; Rahaman MM; Grzegorzek M; Li C (2025). Frontiers in Medicine, 12.
8. Few-shot learning based histopathological image classification of colorectal cancer. Li R; Li X; Sun H; Yang J; Rahaman M; Grzegozek M; Jiang T; Huang X; Li C (2024). Intelligent Medicine, 4, pp. 256–267.
9. 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. Yu Y; Li X; Du T; Rahaman M; Grzegorzek MJ; Li C; Sun H (2024). Intelligent Medicine, 4, pp. 153–160.
10. Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer: a comprehensive comparative study. 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). Intelligent Medicine, 4, pp. 114–127.
11. What can machine vision do for lymphatic histopathology image analysis: a comprehensive review. Chen H; Li X; Li C; Rahaman MM; Li X; Wu J; Sun H; Grzegorzek M; Li X (2024). Artificial Intelligence Review, 57.
12. Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning. Rahaman MM; Millar EKA; Meijering E (2023). Scientific Reports, 13, pp. 13604. Top 100 Cancer Research 2023 ESI Highly Cited
13. EBHI: A new Enteroscope Biopsy Histopathological H&E Image Dataset for image classification evaluation. Hu W; Li C; Rahaman MM; Chen H; Liu W; Yao Y; Sun H; Grzegorzek M; Li X (2023). Physica Medica, 107.
14. A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches. Ma P; Li C; Rahaman MM; Yao Y; Zhang J; Zou S; Zhao X; Grzegorzek M (2023). Artificial Intelligence Review, 56, pp. 1627–1698.
15. A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements. Zhang J; Li C; Rahaman MM; Yao Y; Ma P; Zhang J; Zhao X; Jiang T; Grzegorzek M (2023). Archives of Computational Methods in Engineering, 30, pp. 639–673.
16. Segmentation of weakly visible environmental microorganism images using pair-wise deep learning features. Kulwa F; Li C; Grzegorzek M; Rahaman MM; Shirahama K; Kosov S (2023). Biomedical Signal Processing and Control, 79.
17. CVM-Cervix: A hybrid cervical Pap-smear image classification framework using CNN, visual transformer and multilayer perceptron. 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). Pattern Recognition, 130.
18. GasHis-Transformer: A multi-scale visual transformer approach for gastric histopathological image detection. Chen H; Li C; Wang G; Li X; Rahaman MM; Sun H; Hu W; Li Y; Liu W; Sun C; Ai S; Grzegorzek M (2022). Pattern Recognition, 130.
19. A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches. Li X; Li C; Rahaman MM; Sun H; Li X; Wu J; Yao Y; Grzegorzek M (2022). Artificial Intelligence Review, 55, pp. 4809–4878.
20. A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification. Li Y; Wu X; Li C; Li X; Chen H; Sun C; Rahaman MM; Yao Y; Zhang Y; Jiang T (2022). Applied Intelligence, 52, pp. 9717–9738.
21. An Application of Pixel Interval Down-Sampling (PID) for Dense Tiny Microorganism Counting on Environmental Microorganism Images. Zhang J; Zhao X; Jiang T; Rahaman MM; Yao Y; Lin YH; Zhang J; Pan A; Grzegorzek M; Li C (2022). Applied Sciences Switzerland, 12.
22. EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification, and Detection Method Evaluation. Zhao P; Li C; Rahaman MM; Xu H; Ma P; Yang H; Sun H; Jiang T; Xu N; Grzegorzek M (2022). Frontiers in Microbiology, 13.
23. A comprehensive review of image analysis methods for microorganism counting: from classical image processing to deep learning approaches. Zhang J; Li C; Rahaman MM; Yao Y; Ma P; Zhang J; Zhao X; Jiang T; Grzegorzek M (2022). Artificial Intelligence Review, 55, pp. 2875–2944.
24. IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach. Chen H; Li C; Li X; Rahaman MM; Hu W; Li Y; Liu W; Sun C; Sun H; Huang X; Grzegorzek M (2022). Computers in Biology and Medicine, 143.
25. A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6). Zhao P; Li C; Rahaman MM; Xu H; Yang H; Sun H; Jiang T; Grzegorzek M (2022). Frontiers in Microbiology, 13.
26. GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer. 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). Computers in Biology and Medicine, 142.
27. Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification. Liu W; Li C; Rahaman MM; Jiang T; Sun H; Wu X; Hu W; Chen H; Sun C; Yao Y; Grzegorzek M (2022). Computers in Biology and Medicine, 141.
28. A Comprehensive Review of Markov Random Field and Conditional Random Field Approaches in Pathology Image Analysis. Li Y; Li C; Li X; Wang K; Rahaman MM; Sun C; Chen H; Wu X; Zhang H; Wang Q (2022). Archives of Computational Methods in Engineering, 29, pp. 609–639.
29. SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis. Chen A; Li C; Zou S; Rahaman MM; Yao Y; Chen H; Yang H; Zhao P; Hu W; Liu W; Grzegorzek M (2022). Biocybernetics and Biomedical Engineering, 42, pp. 204–214.
30. DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques. Rahaman MM; Li C; Yao Y; Kulwa F; Wu X; Li X; Wang Q (2021). Computers in Biology and Medicine, 136. ESI Highly Cited
31. EMDS-5: Environmental Microorganism image dataset Fifth Version for multiple image analysis tasks. Li Z; Li C; Yao Y; Zhang J; Rahaman M; Xu H; Kulwa F; Lu B; Zhu X; Jiang T (2021). PLOS ONE, 16.
32. A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development. Ai S; Li C; Li X; Jiang T; Grzegorzek M; Sun C; Rahaman MM; Zhang J; Yao Y; Li H (2021). BioMed Research International, 2021.
33. Gastric histopathology image segmentation using a hierarchical conditional random field. Sun C; Li C; Zhang J; Rahaman MM; Ai S; Chen H; Kulwa F; Li Y; Li X; Jiang T (2020). Biocybernetics and Biomedical Engineering, 40, pp. 1535–1555.
34. A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. 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). IEEE Access, 8, pp. 90931–90956.
35. A survey for cervical cytopathology image analysis using deep learning. Rahaman MM; Li C; Wu X; Yao Y; Hu Z; Jiang T; Li X; Qi S (2020). IEEE Access, 8, pp. 61687–61710. ESI Highly Cited
36. An Application of Transfer Learning and Ensemble Learning Techniques for Cervical Histopathology Image Classification. Xue D; Zhou X; Li C; Yao Y; Rahaman MM; Zhang J; Chen H; Zhang J; Qi S; Sun H (2020). IEEE Access, 8, pp. 104603–104618.
37. An enhanced framework of generative adversarial networks (EF-GANs) for environmental microorganism image augmentation with limited rotation-invariant training data. Xu H; Li C; Rahaman MM; Yao Y; Li Z; Zhang J; Kulwa F; Zhao X; Qi S; Teng Y (2020). IEEE Access, 8, pp. 187455–187469.
38. Foldover Features for Dynamic Object Behaviour Description in Microscopic Videos. 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). IEEE Access, 8, pp. 114519–114540.
39. Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches. Rahaman MM; Li C; Yao Y; Kulwa F; Rahman MA; Wang Q; Qi S; Kong F; Zhu X; Zhao X (2020). Journal of X-Ray Science and Technology, 28, pp. 821–839. ESI Highly Cited

Conference Proceedings

1. A GAN-Based Data Augmentation Method for Mitigating Class Imbalance Problem in Histopathological Image Classification. Yuan L; Rahaman M; Sun H; Li C; Gu Y; Jiang T; Grzegorzek M; Li X (2024). IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024), pp. 5327–5334.
2. ACTIVE: A Deep Network for Sperm and Impurity Detection in Microscopic Videos. Chen A; Fan FL; Zhang J; Rahaman MM; Li R; Tao J; Zeng T; Grzegorzek M; Li C (2024). IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2024), pp. 5247–5256.
3. Histopathology Image Classification Using Supervised Contrastive Deep Learning. Rahaman MM; Millar EKA; Meijering E (2024). Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI 2024).
4. TOD-Net: Transformer-Based Neural Network for Tiny Object Detection in Sperm Microscopic Videos. Zhang J; Zou S; Li C; Yao Y; Rahaman M; Qian W; Sun H; Grzegorzek M; Wang G (2023). Proceedings of the IEEE International Symposium on Biomedical Imaging (ISBI 2023).
5. An Extended Few-Shot Learning-Based Approach for Histopathological Image Classification of Pan-Cancer in the Digestive System. Li R; Rahaman MM; Li X; Sun H; Yang J; Gao M; Grzegozek M; Jiang T; Huang X; Li C (2025). Advanced Data Mining and Applications (ADMA 2024), pp. 140–154.
6. MRes-CNN: A Multi-branch Residual CNN for Colorectal Histopathological Image Classification. Yuan L; Rahaman MM; Sun H; Li X; Grzegorzek M; Xu N; Li C (2025). Advanced Data Mining and Applications (ADMA 2024), pp. 125–139.
7. RBMO-Att-Bi-LSTM: A Red-Billed Blue Magpie Optimiser-Self-attention Mechanism Based Optimisation of Bi-Directional Long- and Short-Term Memory Networks for Classification of COVID-19 CT Images. Sun Y; Du T; Sun H; Xu J; Rahaman MM; Wang X; Huang X; Jiang T; Grzegorzek M; Xu N; Li C (2025). Advanced Data Mining and Applications (ADMA 2024), pp. 171–185.
8. RPE-Diff: A Relative Position Encoding Diffusion Model for Perirenal Fat Segmentation in Metabolic Syndrome. Ye S; Du T; Kulwa F; Meng X; Rahaman MM; Grzegorzek M; Xu N; Jiang T; Sun H; Li C (2025). Advanced Data Mining and Applications (ADMA 2024), pp. 155–170.
9. Intelligent Gastric Histopathology Image Classification Using Hierarchical Conditional Random Field based Attention Mechanism. Li Y; Wu X; Li C; Sun C; Li X; Rahaman M; Zhang Y (2021). 13th International Conference on Machine Learning and Computing (ICMLC ’21), ACM, pp. 330–335.
10. CZTS Based Thin Film Solar Cell: An Investigation into the Influence of Dark Current on Cell Performance. Rahaman MM; Chowdhury A; Islam M; Rahman MM (2018). Joint 7th ICIEV & 2nd icIVPR, Kitakyushu, Japan, pp. 87–92.

Conference Presentations (Selected)

• NeurIPS 2025, San Diego, USA (Oral Presentation – SLC-PFM Challenge Track): “PathDFM: Pathology Distillation Foundation Model for Multi-Scale WSI Analysis.” Selected as a top-performing submission in the Self-supervised Learning for Cancer Pathology Foundation Models competition.
• ASCO Annual Meeting 2026, Chicago, IL, USA (Oral Presentation): “Spatial transcriptomics-guided computational pathology model stratifies docetaxel benefit in metastatic hormone-sensitive prostate cancer: CHAARTED trial (ECOG-ACRIN E3805).”
• ASCO Annual Meeting 2026, Chicago, IL, USA (Abstract Submitted): “Spatial transcriptomics-guided pathology biomarker predicts benefit of adjuvant docetaxel in high-risk localized prostate cancer: NRG/RTOG 0521 (NCT00288080).”
• Advanced Data Mining and Applications (ADMA) 2024, Sydney, Australia (Oral Presentations): Multiple papers on histopathological image classification and deep learning.
• IEEE ISBI 2024, Athens, Greece: “MM-SURVNET: Deep Learning-Based Survival Risk Stratification in Breast Cancer Through Multimodal Data Fusion.”
• Australasian Computer Science Week, UNSW Sydney, 2024: “Predicting Gene Expression in Breast Cancer Using BrST-Net and Spatial Transcriptomics.”
• AI Symposium, UNSW Sydney, 2023: Poster on predicting gene expression in breast cancer using spatial transcriptomics.

Manuscripts Under Review / In Preparation

1. Spatial transcriptomics guided stratification of docetaxel benefit in prostate cancer from routine H&E histopathology. Rahaman MM et al. (Under Review)
2. AI-Driven Multi-Modal Histopathological Biomarker for Prognosis and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma. Rahaman MM et al. (In Preparation)
3. Spatial Transcriptomics and Deep Learning Predict Docetaxel Benefit in High-Risk Prostate Cancer. Rahaman MM et al. (In Preparation)
4. AI Driven Frugality in Emergency Medicine using LLM. Rahaman MM et al. (In Preparation)