Publications by Costantino Grana

Overcrowding in Emergency Departments (EDs) is a pressing concern driven by high patient demand and limited resources. Prolonged Length of Stay (pLOS), a major contributor to this congestion, may lead to adverse outcomes, including patients leaving without being seen, suboptimal clinical care, increased mortality rates, provider burnout, and escalating healthcare costs. This study investigates the application of various Machine Learning (ML) algorithms to predict both LOS and pLOS. A retrospective analysis examined 32,967 accesses at a northern Italian hospital’s ED between 2022 and 2024. Twelve classification algorithms were evaluated in forecasting pLOS, using clinically relevant thresholds. Two data variants were employed for model comparison: one containing only structured data (e.g., demographics and clinical information), while a second one also including features extracted from free-text nursing notes. To enhance the accuracy of LOS prediction, novel queue-based variables capturing the real-time state of the ED were incorporated as additional dynamic predictors. Compared to single-algorithm models, ensemble models demonstrated superior robustness in forecasting both ED-LOS and ED-pLOS. These findings highlight the potential for integrating ML into EDs practices as auxiliary tools to provide valuable insights into patient flow. By identifying patients at high risk of pLOS, healthcare professionals can proactively implement strategies to expedite care, optimize resource allocation, and ultimately improve patient outcomes and ED efficiency, promoting a more effective and sustainable public healthcare delivery.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surfacemodels are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models). However, a large collection of anatomical shapes (e.g., bones, organs, vessels) and 3D models of surgical instruments is missing. Methods: We present MedShapeNet to translate datadriven vision algorithms to medical applications and to adapt state-of-the-art vision algorithms to medical problems. As a unique feature, we directly model the majority of shapes on the imaging data of real patients. We present use cases in classifying brain tumors, skull reconstructions, multi-class anatomy completion, education, and 3D printing. Results: By now, MedShapeNet includes 23 datasets with more than 100,000 shapes that are paired with annotations (ground truth). Our data is freely accessible via aweb interface and a Python application programming interface and can be used for discriminative, reconstructive, and variational benchmarks as well as various applications in virtual, augmented, or mixed reality, and 3D printing. Conclusions: MedShapeNet contains medical shapes from anatomy and surgical instruments and will continue to collect data for benchmarks and applications. The project page is: https://medshapenet.ikim.nrw/.

Recently, Multimodal Large Language Models (MLLMs) have emerged as a leading framework for enhancing the ability of Large Language Models (LLMs) to interpret non-linguistic modalities. Despite their impressive capabilities, the robustness of MLLMs under conditions where one or more modalities are missing remains largely unexplored. In this paper, we investigate the extent to which MLLMs can maintain performance when faced with missing modality inputs. Moreover, we propose a novel framework to mitigate the aforementioned issue called Retrieval-Augmented Generation for missing modalities (MissRAG). It consists of a novel multimodal RAG technique alongside a tailored prompt engineering strategy designed to enhance model robustness by mitigating the impact of absent modalities while preventing the burden of additional instruction tuning. To demonstrate the effectiveness of our techniques, we conducted comprehensive evaluations across five diverse datasets, covering tasks such as audio-visual question answering, audio-visual captioning, and multimodal sentiment analysis.

QR and Datamatrix codes are widely used in warehouse logistics and high-speed production pipelines. Still, distant or small barcodes often yield low-pixel-density images that are hard to read. Conventional solutions rely on costly hardware or enhanced lighting, raising expenses and potentially reducing depth of field. We propose Mosaic-SR, a multi-step, adaptive super-resolution (SR) method that devotes more computation to barcode regions than uniform backgrounds. For each patch, it predicts an uncertainty value to decide how many refinement steps are required. Our experiments show that Mosaic-SR surpasses state-of-the-art SR models on 2D barcode images, achieving higher PSNR and decoding rates in less time. All code and trained models are publicly available at https://github.com/Henvezz95/mosaic-sr.

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.

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.

Cone-beam computed tomography (CBCT) is a standard imaging modality in orofacial and dental practices, providing essential 3D volumetric imaging of anatomical structures, including jawbones, teeth, sinuses, and neurovascular canals. Accurately segmenting these structures is fundamental to numerous clinical applications, such as surgical planning and implant placement. However, manual segmentation of CBCT scans is time-intensive and requires expert input, creating a demand for automated solutions through deep learning. Effective development of such algorithms relies on access to large, well-annotated datasets, yet current datasets are often privately stored or limited in scope and considered structures, especially concerning 3D annotations. This paper proposes ToothFairy2, a comprehensive, publicly accessible CBCT dataset with voxel-level 3D annotations of 42 distinct classes corresponding to maxillofacial structures. We validate the dataset by benchmarking state-of-the-art neural network models, including convolutional, transformer-based, and hybrid Mamba-based architectures, to evaluate segmentation performance across complex anatomical regions. Our work also explores adaptations to the nnU-Net framework to optimize multi-class segmentation for maxillofacial anatomy. The proposed dataset provides a fundamental resource for advancing maxillofacial segmentation and supports future research in automated 3D image analysis in digital dentistry.

In recent years, several algorithms have been developed for the segmentation of the Inferior Alveolar Canal (IAC) in Cone-Beam Computed Tomography (CBCT) scans. However, the availability of public datasets in this domain is limited, resulting in a lack of comparative evaluation studies on a common benchmark. To address this scientific gap and encourage deep learning research in the field, the ToothFairy challenge was organized within the MICCAI 2023 conference. In this context, a public dataset was released to also serve as a benchmark for future research. The dataset comprises 443 CBCT scans, with voxel-level annotations of the IAC available for 153 of them, making it the largest publicly available dataset of its kind. The participants of the challenge were tasked with developing an algorithm to accurately identify the IAC using the 2D and 3D-annotated scans. This paper presents the details of the challenge and the contributions made by the most promising methods proposed by the participants. It represents the first comprehensive comparative evaluation of IAC segmentation methods on a common benchmark dataset, providing insights into the current state-of-the-art algorithms and outlining future research directions. Furthermore, to ensure reproducibility and promote future developments, an open-source repository that collects the implementations of the best submissions was released.

This paper tackles the domain of multimodal prompting for visual recognition, specifically when dealing with missing modalities through multimodal Transformers. It presents two main contributions: (i) we introduce a novel prompt learning module which is designed to produce sample-specific prompts and (ii) we show that modality-agnostic prompts can effectively adjust to diverse missing modality scenarios. Our model, termed SCP, exploits the semantic representation of available modalities to query a learnable memory bank, which allows the generation of prompts based on the semantics of the input. Notably, SCP distinguishes itself from existing methodologies for its capacity of self-adjusting to both the missing modality scenario and the semantic context of the input, without prior knowledge about the specific missing modality and the number of modalities. Through extensive experiments, we show the effectiveness of the proposed prompt learning framework and demonstrate enhanced performance and robustness across a spectrum of missing modality cases.

Barcodes, despite their long history, remain an essential technology in supply chain management. In addition, barcodes have found extensive use in industrial engineering, particularly in warehouse automation, component tracking, and robot guidance. To detect a barcode in an image, multiple algorithms have been proposed in the literature, with a significant increase of interest in the topic since the rise of deep learning. However, research in the field suffers from many limitations, including the scarcity of public datasets and code implementations which hinders the reproducibility and reliability of published results. For this reason, we developed ``BarBeR'' (Barcode Benchmark Repository), a benchmark designed for testing and comparing barcode detection algorithms. This benchmark includes the code implementation of various detection algorithms for barcodes, along with a suite of useful metrics. Among the supported localization methods, there are multiple deep-learning detection models, that will be used to assess the recent contributions of Artificial Intelligence to this field. In addition, we provide a large, annotated dataset of 8748 barcode images, combining multiple public barcode datasets with standardized annotation formats for both detection and segmentation tasks. Finally, we provide a thorough summary of the history and literature on barcode localization and share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms when applied to real-world problems.