You can also find my articles on my Google Scholar profile.
A step-by-step guide to the analysis and interpretation of biomedical data, covering the computational and statistical methods used in modern biomedical research.
Cascino D.L., Gatti G., Unwith S., Matarazzo L.S., Riva S.G., Damiani G., Tangherloni A. (2025). Biomedical Data Science: A Step-by-Step Guide to Analysis and Interpretation. River Publishers.
A study of the generalization ability of CNNs (SegNet, U-Net, pix2pix) for prostate central-gland and peripheral-zone segmentation across two multi-centric MRI datasets.
Rundo L., Han C., Zhang J., Hataya R., Nagano Y., Militello C., Ferretti C., Nobile M.S., Tangherloni A., Gilardi M.C., Vitabile S., Nakayama H., Mauri G. (2020). CNN-Based Prostate Zonal Segmentation on T2-Weighted MR Images: A Cross-Dataset Study. In Neural Approaches to Dynamics of Signal Exchanges, 151: 269-280, Springer.
Computer-assisted approaches for uterine fibroid segmentation in MRgFUS treatments, with a quantitative evaluation of the methods and a clinical feasibility analysis.
Rundo L., Militello C., Tangherloni A., Russo G., Lagalla R., Mauri G., Gilardi M.C., Vitabile S. (2018). Computer-assisted Approaches for Uterine Fibroid Segmentation in MRgFUS Treatments: Quantitative Evaluation and Clinical Feasibility Analysis. In Quantifying and Processing Biomedical and Behavioral Signals, Smart Innovation, Systems and Technologies, 103: 229-241, Springer.
A chapter on accelerating stochastic simulations of mechanistic biological models on GPUs, discussing the advantages and the practical issues of parallelization.
Cazzaniga P., Nobile M.S., Tangherloni A., Besozzi D. (2018). Accelerating stochastic simulations of mechanistic models of biological systems: Advantages and issues in the parallelization on Graphics Processing Units. In Quantitative Biology: Theory, Computational Methods, and Models, 423-440, MIT Press.
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