Publications

The synthesis of missing MRI modalities has emerged as a critical solution to address incomplete multi-parametric imaging in brain tumor diagnosis and treatment planning. While recent advances in generative models, especially GANs and diffusion-based approaches, have demonstrated promising results in cross-modality MRI generation, challenges remain in preserving anatomical fidelity and minimizing synthesis artifacts. In this work, we build upon the Hybrid Fusion GAN (\hfgan) framework, introducing several enhancements aimed at improving synthesis quality and generalization across tumor types. Specifically, we incorporate z-score normalization, optimize network components for faster and more stable training, and extend the pipeline to support multi-view generation across various brain tumor categories, including gliomas, metastases, and meningiomas. Our approach focuses on refining 2D slice-based generation to ensure intra-slice coherence and reduce intensity inconsistencies, ultimately supporting more accurate and robust tumor segmentation in scenarios with missing imaging modalities. Our source code is available at https://github.com/AImageLab-zip/BraSyn25.

There is a recent growing interest in applying Deep Learning techniques to tabular data in order to replicate the success of other Artificial Intelligence areas in this structured domain. Particularly interesting is the case in which tabular data have a time dependence, such as, for instance, financial transactions. However, the heterogeneity of the tabular values, in which categorical elements are mixed with numerical features, makes this adaptation difficult. In this paper we propose UniTTab, a Transformer based architecture whose goal is to uniformly represent heterogeneous time-dependent tabular data, in which both numerical and categorical features are described using continuous embedding vectors. Moreover, differently from common approaches, which use a combination of different loss functions for training with both numerical and categorical targets, UniTTab is uniformly trained with a unique Masked Token pretext task. Finally, UniTTab can also represent time series in which the individual row components have a variable internal structure with a variable number of fields, which is a common situation in many application domains, such as in real world transactional data. Using extensive experiments with five datasets of variable size and complexity, we empirically show that UniTTab consistently and significantly improves the prediction accuracy over several downstream tasks and with respect to both Deep Learning and more standard Machine Learning approaches. Our code and our models are available at: https://github.com/fabriziogaruti/UniTTab.

Effective hospital resource management hinges on established metrics such as Length of Stay (LOS) and Prolonged Length of Stay (pLOS). Reducing pLOS is associated with improved patient outcomes and optimized resource utilization (e.g., bed allocation). This study investigates several Machine Learning (ML) models for both LOS and pLOS prediction. We conducted a retrospective study analyzing data from general inpatients discharged between 2022 and 2023 at a northern Italian hospital. Sixteen regression and twelve classification algorithms were compared in forecasting LOS as either a continuous or multi-class variable (1-3 days, 4-10 days, >10 days). Additionally, the same models were assessed for pLOS prediction (defined as LOS exceeding 8 days). All models were evaluated using two variants of the same dataset: one containing only structured data (e.g., demographics and clinical information), and a second one also containing features extracted from free-text diagnosis. Ensemble models, leveraging the combined strengths of multiple ML algorithms, demonstrated superior accuracy in predicting both LOS and pLOS compared to single-algorithm models, particularly when utilizing both structured and unstructured data extracted from diagnoses. Integration of ML, particularly ensemble models, has the potential to significantly improve LOS prediction and identify patients at high risk of pLOS. Such insights can empower healthcare professionals and bed managers to optimize patient care and resource allocation, promoting overall healthcare efficiency and sustainability.

Video Question Answering (Video QA) is a critical and challenging task in video understanding, necessitating models to comprehend entire videos, identify the most pertinent information based on the contextual cues from the question, and reason accurately to provide answers. Initial endeavors in harnessing Multimodal Large Language Models (MLLMs) have cast new light on Visual QA, particularly highlighting their commonsense and temporal reasoning capacities. Models that effectively align visual and textual elements can offer more accurate answers tailored to visual inputs. Nevertheless, an unresolved question persists regarding video content: How can we efficiently extract the most relevant information from videos over time and space for enhanced VQA? In this study, we evaluate the efficacy of various temporal modeling techniques in conjunction with MLLMs and introduce a novel component, T-Former, designed as a question-guided temporal querying transformer. T-Former bridges frame-wise visual perception and the reasoning capabilities of LLMs. Our evaluation across various VideoQA benchmarks shows that T-Former, with its linear computational complexity, competes favorably with existing temporal modeling approaches and aligns with the latest advancements in Video QA tasks.

Cross-modal retrieval is gaining increasing efficacy and interest from the research community, thanks to large-scale training, novel architectural and learning designs, and its application in LLMs and multimodal LLMs. In this paper, we move a step forward and design an approach that allows for multimodal queries -- composed of both an image and a text -- and can search within collections of multimodal documents, where images and text are interleaved. Our model, ReT, employs multi-level representations extracted from different layers of both visual and textual backbones, both at the query and document side. To allow for multi-level and cross-modal understanding and feature extraction, ReT employs a novel Transformer-based recurrent cell that integrates both textual and visual features at different layers, and leverages sigmoidal gates inspired by the classical design of LSTMs. Extensive experiments on M2KR and M-BEIR benchmarks show that ReT achieves state-of-the-art performance across diverse settings. Our source code and trained models are publicly available at: https://github.com/aimagelab/ReT.

Efficient energy provisioning is a fundamental requirement for modern transportation systems, making refueling path optimization a critical challenge. Existing solutions often focus either on inter-vehicle communication or intravehicle monitoring, leveraging Intelligent Transportation Systems, Digital Twins, and Software-Defined Internet of Vehicles with Cloud/Fog/Edge infrastructures. However, integrated frameworks that adapt dynamically to driver mobility patterns are still underdeveloped. Building on our previous PIENO framework, we present RI-PIENO (Revised and Improved Petrolfilling Itinerary Estimation aNd Optimization), a system that combines intra-vehicle sensor data with external geospatial and fuel price information, processed via IoT-enabled Cloud/Fog services. RI-PIENO models refueling as a dynamic, time-evolving directed acyclic graph that reflects both habitual daily trips and real-time vehicular inputs, transforming the system from a static recommendation tool into a continuously adaptive decision engine. We validate RI-PIENO in a daily-commute use case through realistic multi-driver, multi-week simulations, showing that it achieves significant cost savings and more efficient routing compared to previous approaches. The framework is designed to leverage emerging roadside infrastructure and V2X communication, supporting scalable deployment within next-generation IoT and vehicular networking ecosystems.