Research Engineer | PhD Applicant in AI for Healthcare
Multimodal AI for healthcare, computational pathology, and medical image analysis.
I am a Research Engineer at Institut Curie - PSL Research University in Paris, where I develop computational pipelines using deep learning and statistical methods to study the tumor microenvironment from histopathology and spatial transcriptomics data. My research focus on developing multimodal AI integrating radilogy, pathology, multi-omics, and clinical information to advance perssonalized healthcare.
I hold a double master's degree: an Erasmus+ Joint Master's in Medical Imaging and AI (MaIA), followed by a Master in Bioinformatics at Sorbonne University. My graduate training spans France, Italy, and Spain, and I have contributed to impactful research at Institut Curie, CNRS, and RadboudUMC. I completed my B.Sc. in Electrical Engineering from UIU, graduating Summa Cum Laude and receiving the Chancellor's Award.
My academic and research training has been supported by competitive funding, including the EU-funded Erasmus Mundus scholarship, the Acquisis International Mobility Grant from the RBFC, and research internships at CNRS and Institut Curie. My work has been recognized with distinctions including the Best Thesis Award. Beyond research, I am actively engaged in academic and professional communities through the Erasmus Mundus Association and IEEE.
Research
My research focuses on multimodal deep learning methods that integrate tissue morphology with molecular data, combining histopathology imaging, spatial transcriptomics, and multi-omics signals to extract clinically actionable information from cancer samples. I am particularly interested in weakly supervised and self-supervised approaches that work under the label scarcity constraints of real clinical datasets, while also maintaining a strong interest in medical image analysis problems such as segmentation, registration, reconstruction, and AI-assisted diagnosis.
At Institut Curie I am currently analyzing Visium HD spatial transcriptomics data alongside H&E histopathology to characterize fibroblast-mediated immune exclusion in lung cancer. Prior work at CNRS benchmarked attention-based multiple instance learning and foundation models for HRD detection in breast and ovarian cancer, achieving AUC 0.78 on breast cancer whole-slide images.
Weakly supervised MIL, foundation models, and whole-slide image analysis for biomarker detection and tumor microenvironment characterization.
Visium HD spatial transcriptomics pipelines, cell type deconvolution, and integration with histology for molecular tissue mapping.
MRI reconstruction, image registration, and segmentation with deep learning, including real-time reconstruction for interventional radiology.
Highlights
Joined Institut Curie - PSL Research University as Research Engineer in Bioinformatics and Artificial Intelligence.
Graduated ranked 1st with Best Thesis Award (Tres Bien) from Sorbonne University, Master's in Bioinformatics and Modeling.
Identified eight candidate genes mediating CAF-driven immune exclusion in lung squamous cell carcinoma in an Institut Curie / Sanofi collaboration. Manuscript in preparation.
Ranked 2nd in MAIA Brain Tissue Segmentation Challenge, University of Girona (26 participants).
Awarded Erasmus Mundus Scholarship (EUR 42,000) by the European Union for a competitive joint master's across three European universities.
Education
Sorbonne University, France | Grade: 14.85/20 | Rank: 1st of 8 | Best Thesis Award
Thesis: Identification of Tumor Gene Signatures Underlying Fibroblast-Mediated T Cell Exclusion in Lung Cancer Using Spatial Transcriptomics.
ETH Zurich & University of Zurich, Switzerland | 4 ECTS
University of Girona, University of Cassino, and University of Burgundy | Grade: 8.30/10
Fully funded by the European Union (EUR 42,000).
United International University, Bangladesh | GPA: 3.97/4.00 | Summa Cum Laude | Rank: 1st of 120
Experience
Institut Curie - PSL Research University, Paris, France
Leading development of multimodal computational pipelines integrating histopathology and spatial transcriptomics to study tumor microenvironment dynamics. Collaborating with clinical partners and preparing manuscripts.
Institut Curie / Sanofi collaboration, Paris, France
Developed analysis pipelines for Visium V2 and Visium HD spatial transcriptomics data. Identified eight candidate genes potentially mediating CAF-driven T cell exclusion in lung squamous cell carcinoma. Manuscript in preparation.
National Center for Scientific Research (CNRS), France
Benchmarked fully supervised, weakly supervised, and self-supervised approaches for HRD detection in breast and ovarian cancer whole-slide images. Best model achieved AUC 0.78 on breast cancer and 0.68 on ovarian cancer.
Diagnostic Image Analysis Group, Radboud University Medical Center, Netherlands
Developed preprocessing pipelines for k-space undersampling and evaluated deep learning models for real-time MRI reconstruction in interventional radiology.
Preprints & Manuscripts
Hossain M.I. et al. Institut Curie / Sanofi collaboration. Manuscript in preparation, 2026.
Hossain M.I., Amin M.Z., et al. arXiv:2411.05456
Tweneboah A.D., Hossain M.I. (co-author), et al. arXiv:2502.19258
Projects
Visium HD pipeline for identifying CAF-mediated T cell exclusion signatures in lung squamous cell carcinoma. Identified 8 candidate genes. Manuscript in preparation.
Benchmarked AB-MIL, CLAM, Trans-MIL, and foundation models for homologous recombination deficiency detection. Achieved AUC 0.78 on breast cancer whole-slide images.
ArXiv preprint
nnU-Net pipeline for automated echocardiogram segmentation. Dice scores: LV endocardium 0.94, LV epicardium 0.91, left atrium 0.93.
XGBoost pipeline integrating DNA methylation, CNV, mRNA, and miRNA data for breast cancer subtyping. Balanced multiclass accuracy: 0.90.
