Thesis Supervision (75 items)

Students I have supervised or co-supervised for their Bachelor's, Master's and PhD theses.

PhD Theses (7 students)

2025 - 2029

Giovanni Casari

Ongoing

PhD Thesis – Information and Communication Technologies

2025 - 2029

Nicola Morelli

Ongoing

PhD Thesis – Information and Communication Technologies

2025 - 2029

Omar Carpentiero

Ongoing

PhD Thesis – Information and Communication Technologies

2024 - 2028

Ettore Candeloro

Ongoing

PhD Thesis – Information and Communication Technologies

2023 - 2027

Kevin Marchesini

Ongoing

PhD Thesis – Communication Technologies, Artificial Intelligence Engineering

2022 - 2026

Enrico Vezzali

Efficient and Adaptive Deep Learning Methods for Automatic Data Capture Systems

PhD Thesis – Information and Communication Technologies

2022 - 2026

Luca Lumetti

Scale AI for Oral and Dental Image Analysis

PhD Thesis – Information and Communication Technologies

Master's Theses (13 students)

April 2026

Davide Santoli – MSc

Paper-snitch: A Practical Tool for Evidence-Based Reproducibility Assessment

Master's Thesis – Artificial Intelligence Engineering

Abstract: Reproducibility policies aim to make medical-imaging research “checkable” at review time, yet many submissions still ship artifacts that are missing, incomplete, or not practically verifiable under peer-review constraints. In a longitudinal analysis of 3,722 MICCAI papers, the fraction linking code increases from 51.8% to 72.5%, but ~13% of linked repositories are inaccessible or empty, and many others lack the concrete information needed to audit claims without executing untrusted code. This thesis introduces PAPER-SNITCH, a reviewer-facing decision support for evidence-based reproducibility screening. PAPER-SNITCH parses conference metadata and PDFs, resolves and sanity-checks linked repositories, and applies a policy-aware checklist aligned with MICCAI-style guidance. Rather than attempting full reproduction, it performs bounded, inspectable checks (e.g., artifact presence, documentation completeness, environment specification, and claim-to-command traceability) and produces an auditable report that links each criterion outcome to concrete evidence excerpts and repository artifacts. Criterion outcomes are aggregated deterministically into interpretable component scores and a global verifiability summary. We evaluate PAPER-SNITCH against human assessment on a sample of MICCAI papers and analyze practical deployment considerations, including per-paper cost, caching, and observability. Finally, we discuss key limitations—most notably incomplete recall under bounded retrieval, susceptibility to strategically written text, and the non-determinism and drift of LLM-backed components—and outline directions for robustness hardening and reviewer-centered validation.

February 2026

Matteo Lugli – MSc, currently Research Fellow

An Automated Dental Bracket Positioning Model: A Tailored AI Solution for Clinical Orthodontic Practice

Master's Thesis – Artificial Intelligence Engineering

Abstract: Accurate positioning of orthodontic brackets is a critical step in fixed appliance therapy, as placement errors directly affect treatment efficiency, duration, and clinical outcomes. Recent advances in digital orthodontics and intra-oral scanning enable the automation of this process through data-driven methods. This thesis presents a deep learning framework for predicting dental bracket installation points directly from intra-oral scans of patients. The proposed approach operates on three-dimensional dental data and leverages point cloud representations to model tooth geometry. A complete pipeline is developed, including data collection, preprocessing, tooth segmentation, and bracket position prediction. Multiple prediction strategies are investigated, including direct regression and heatmap-based formulations, and their performance is evaluated on curated datasets derived from clinical annotations. To support model deployment in real-world scenarios, a dedicated segmentation model is designed and integrated, providing improved flexibility and control compared to off-the-shelf solutions. Extensive experimental results demonstrate that the proposed methods achieve accurate and consistent bracket placement predictions, approaching clinically acceptable precision. Additionally, the system is packaged and deployed through a web-based API, enabling seamless integration with existing orthodontic software and workflows. This work highlights the feasibility of automated bracket placement from intra-oral scans and provides a scalable foundation for future improvements through additional expert annotations, model refinement, and extended evaluation of segmentation performance. The proposed framework contributes toward reducing manual effort and variability in orthodontic bracket positioning, supporting more efficient and standardized digital orthodontic treatments.

October 2025

Nicola Morelli – MSc, currently Ph.D. Student

Enhancing Testicular Ultrasound Image Classification Through Synthetic Data and Pretraining Strategies

Master's Thesis – Artificial Intelligence Engineering

Abstract: Male infertility is a widespread health concern, with testicular ultrasound imaging playing a key role in its assessment. In particular, parenchymal inhomogeneity has been proposed as a biomarker, but its reliable evaluation is challenged by subjective interpretation, artifacts, and the limited availability of annotated datasets. This thesis addresses these challenges by investigating strategies that combine pretraining and synthetic data generation to enhance automated classification of testicular ultrasound images. A ResNet-18 backbone is employed, and both supervised and self-supervised pretraining strategies are evaluated to improve feature representation in data-scarce conditions. To reduce the impact of noisy labels, a heuristic filtering method is proposed, identifying and correcting mislabeled samples. Furthermore, synthetic ultrasound data are generated using diffusion models, followed by a filtering procedure to ensure fidelity and clinical relevance. Experimental results demonstrate that pretraining consistently improves classification performance compared to training from scratch, while synthetic data can effectively support pretraining, partially overcoming data scarcity and privacy limitations. These findings highlight the potential of integrating synthetic data and pretraining strategies to build robust and reliable diagnostic tools, ultimately contributing to the advancement of automated ultrasound analysis for male infertility.

October 2025

Omar Carpentiero – MSc, currently Ph.D. Student

Synthesis of Missing Brain MRI Modalities for Improved Tumor Segmentation

Master's Thesis – Artificial Intelligence Engineering

Abstract: The synthesis of missing MRI modalities has emerged as a critical strategy to address incomplete multi-parametric imaging in brain tumor diagnosis and treatment planning. Recent advances in generative models, particularly GANs and diffusion-based approaches, have shown promising results in cross-modality MRI generation, although challenges persist in preserving anatomical fidelity and minimizing synthesis artifacts. Building on the Hybrid Fusion GAN (HF-GAN) framework, several enhancements are introduced to improve synthesis quality and generalization across tumor types. These include the application of z-score normalization, optimization of network components for faster and more stable training, and the extension of the pipeline to support multi-view generation across diverse brain tumor categories such as gliomas, metastases, and meningiomas. The approach emphasizes refinement of 2D slice-based generation to ensure intra-slice coherence and reduce intensity inconsistencies, ultimately facilitating more accurate and robust tumor segmentation in scenarios with missing imaging modalities.

March 2023

Ettore Candeloro – MSc, currently Ph.D. Student

Skin Lesion Classification Explained with Generative Adversarial Networks

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

March 2023

Gabriele Rosati – MSc, currently Research Fellow

Prediction of Kidney Failure with Deep Neural Networks Fusing WSI and Immunofluorescence Images

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

March 2022

Luca Lumetti – MSc, currently Ph.D. Student

Inferior Alveolar Canal Segmentation Using Deep Neural Networks

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

March 2020

Cristian Mercadante – MSc

Development of a Cone Beam Computed Tomography Annotation Tool for Automatic Detection of the Inferior Alveolar Nerve Canal through Deep Learning

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

January 2020

Luca Benzi – MSc

Deep Learning Techniques for Medical Imaging

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

January 2020

Maximilian Söchting – MSc, Erasmus+ from the Hasso Plattner Institute of Potsdam University

Using Heuristics for Decision Tree Generation in Image Processing

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

January 2019

Stefano Allegretti – MSc, former Ph.D. Student

Optimization of Connected Components Labeling Algorithms on Binary Images for CPUs and GPUs

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

January 2017

Federico Pollastri – MSc, former Ph.D. Student

Impact of a Generative Adversarial Network synthetic dataset on fully convolutional-deconvolutional networks for automatic skin lesion segmentation training

Master's Thesis – Laurea Magistrale in Ingegneria Informatica

January 2017

Michele Cancilla – MSc, former Research Fellow

Parallelization of Connected Components Labeling Algorithms

Master's Thesis – Laurea Magistrale in Ingegneria Informatica