Mohammad Imran Hossain

I am a Marie Skłodowska-Curie Actions (MSCA) Doctoral Candidate at the Department of Medical Epidemiology and Biostatistics (MEB), Karolinska Institutet in Sweden, as part of the HER-CARE MSCA Doctoral Network.

 

My doctoral research focuses on developing artificial intelligence (AI) models for breast cancer risk prediction using mammography, breast magnetic resonance imaging (MRI), and epidemiological data. The overarching goal is to advance personalized and risk-adapted breast cancer screening strategies, contributing to more accurate risk assessment, earlier detection, and improved clinical decision-making.

Before joining Karolinska Institutet, I worked as a computational oncology researcher at Institut Curie in Paris, France. My research involved developing computational approaches to investigate the tumor microenvironment through the integration of spatial transcriptomics, single-cell RNA sequencing, histopathology, and statistical and machine-learning methods, with a particular focus on tumor–immune interactions and the spatial organization of cancer tissues.

 

I hold a Bachelor's degree in Electrical and Electronic Engineering from United International University in Bangladesh, and two Master's degrees from European universities. I completed the Erasmus Mundus Joint Master's Degree in Medical Imaging and Applications (MaIA), jointly delivered by the University of Burgundy (France), University of Cassino and Southern Lazio (Italy), and University of Girona (Spain), supported by the prestigious Erasmus Mundus scholarship funded by the European Commission. I subsequently earned a second Master's degree in Computer Science, specializing in Bioinformatics and Modeling, from Sorbonne Université in France.

 

My previous research experience spans Institut Curie, French National Centre for Scientific Research (CNRS), and Radboud University Medical Center in the Netherlands. Across these institutions, I have worked on a broad range of biomedical AI applications, including computational analysis of the tumor microenvironment, tumor immune exclusion, biomarker prediction from whole-slide histopathology images, spatial and single-cell omics, real-time MRI reconstruction, and medical image segmentation and registration.

 

My broader research interests lie at the intersection of artificial intelligence, medical imaging, computational oncology, and precision medicine. I am particularly interested in developing robust, interpretable, and multimodal AI approaches that integrate imaging, molecular, clinical, and population-level data to improve cancer risk prediction, early detection, and patient stratification. Through my research, I aim to bridge methodological advances in AI with clinically relevant applications, ultimately contributing to more personalized healthcare and better patient outcomes.