Publications by Federico Bolelli

Reliable estimates of cellular composition and anatomical distribution in the human brain are essential for biologically plausible circuit models. In the hippocampus, existing reconstructions rely on low-resolution (LR) data without explicit cell-type-resolved annotations, limiting quantitative maps of excitatory neurons, inhibitory interneurons, and glial cells. Using newly released 1 um/px BigBrain sections of the right hippocampus, we present CALHippo, Cellular Annotation Library for the Hippocampus, a multiscale resource for cell-type-resolved reconstruction of the human CA complex. CALHippo includes the first expert-validated, cell-level annotated dataset spanning all Cornu Ammonis (CA1-CA4) subfields with explicit three-class labels, together with a lower-resolution mesoscale cellular point-cloud map. High-resolution (HR) cell instances are obtained through a human-in-the-loop pipeline combining foundation-model-based segmentation, iterative expert correction, and model ensembling, and are classified as excitatory neurons, inhibitory interneurons, or glial cells. To extend sparse HR annotations to the full volume, we project them into the 20 um/px LR BigBrain space and use the resulting class-specific supervision maps to train a UNet-based density estimation model. The predicted density maps enable slice-by-slice inference across the full CA complex and are sampled to generate a class-resolved mesoscale cellular point cloud. Code (https://github.com/AImageLab-zip/CALHippo-Framework) and dataset (https://ditto.ing.unimore.it/calhippo) are publicly released to support reproducibility.

Visual Autoregressive (VAR) models face a severe "Memory Wall" on edge devices due to large model size and substantial KV-cache requirements. In this work, we analyze the Infinity VAR family (2B and 8B) and propose a compression pipeline for deployment on constrained NVIDIA Jetson systems. We diagnose critical bottlenecks: activation outliers reaching 353x the median and channel-skewed cache variance. To address this, we propose a hybrid pipeline combining SVDQuant—to structurally decouple weight outliers—and Asymmetric Per-Channel KV8 quantization. Our approach reduces the Infinity-8B footprint by 64% (37.1GB →13.3GB), fitting it on the mid-range Orin NX with a 4.1x speedup over Flux.1-dev (W4A4), while achieving superior aesthetic alignment (ImageReward 1.13 vs 0.935). Crucially, we also unlock entry-level feasibility for the Infinity-2B, compressing it from 16.0 to 7.71 GB to enable deployment on the Orin Nano. These results establish a new efficiency standard for high-fidelity generative AI at the edge. The code is available at https://github.com/Henvezz95/deepcompressor.

Multimodal Large Language Models (MLLMs) have achieved impressive performance across a variety of vision–language tasks. However, their internal working mechanisms remain largely underexplored. In his work, we introduce FG-TRACER, a framework designed to analyze the information flow between visual and textual modalities in MLLMs in free-form generation. Notably, our numerically stabilized computational method enables the first systematic analysis of multimodal information flow in underexplored domains such as image captioning and chain-of-thought (CoT) reasoning. We apply FG-TRACER to two state-of-the-art MLLMs—LLaMA 3.2-Vision and LLaVA 1.5—across three vision–language benchmarks—TextVQA, COCO 2014, and ChartQA—and we conduct a word-level analysis of multimodal integration. Our findings uncover distinct patterns of multimodal fusion across models and tasks, demonstrating that fusion dynamics are both model- and task-dependent. Overall, FG-TRACER offers a robust methodology for probing the internal mechanisms of MLLMs in free-form settings, providing new insights into their multimodal reasoning strategies. Our source code is publicly available at https://anonymous.4open.science/r/FG-TRACER-CB5A/.

High-resolution histological imaging provides essential detail for quantitative brain modeling, yet acquiring whole-brain data at micrometer scale remains technically and economically challenging. This work introduces Brain-SR, a diffusion-based super-resolution framework designed to reconstruct high-resolution cortical sections from low-resolution BigBrain data. Building upon the InvSR paradigm, our method performs resolution enhancement in the latent space of a pretrained variational autoencoder, guided by a task-specific noise-predictor network. A key contribution is a frequency-domain supervision term that compares the magnitude spectra of predicted and target patches, enforcing spectral consistency while remaining robust to local misalignments. Quantitative evaluations demonstrate that Brain-SR achieves substantial improvements in LPIPS (-27%) and FID (-58%) compared to baseline diffusion Super-Resolution, while spectral analysis confirms accurate recovery of the frequency distribution. The resulting reconstructions preserve neuronal structures consistent with high-resolution references, offering a practical step toward large-scale, morphologically faithful brain histology reconstruction. The code is publicly available to support reproducibility: https://github.com/AImageLab-zip/Brain-SR.

Cone-beam computed tomography (CBCT) is widely used for dento-maxillofacial diagnostics and treatment planning, and comprehensive multi-structure segmentation remains time-consuming, limiting large-scale, reproducible research. In this article, we present ToothFairy2, a MICCAI 2024 challenge on multi-structure segmentation in maxillofacial CBCT. The accompanying dataset comprises 530 CBCT volumes (480 public training, 50 hidden test) with expert 3D annotations of 42 classes, including maxilla, mandible, crowns, bridges, implants, inferior alveolar canals, maxillary sinuses, pharynx, and teeth using the International Tooth Numbering System (FDI). 26 international teams participated in ToothFairy2, and their methods were run and evaluated for voxel-wise multi-class segmentation using a standardized protocol. This report extends the evaluation of teeth to also investigate the current capabilities of tooth detection and FDI numbering. Furthermore, ranking stability was analyzed to assess the robustness of the final challenge outcome. Overall, challenge participants achieved consistently high performance for large, high-contrast structures such as jawbones, pharynx, and most teeth, while maxillary sinuses, dental restorations, and fine structures remain challenging due to class imbalance and metal artifacts. Analysis of tooth-related metrics further revealed that assigning correct FDI numbers was more challenging than delineating individual teeth. By releasing CBCT data, 3D annotations, baseline models, and evaluation code, ToothFairy2 establishes a long-term benchmark to drive the development of automated methods for robust, clinically meaningful multi-structure segmentation in maxillofacial CBCT.

We present a dataset and baseline for ontology-grounded structured prediction from dental Cone-Beam Computed Tomography (CBCT) volumes. Building on the public ToothFairy3 benchmark (532 volumes with expert-level segmentations), we contribute (i) a total of 893 free-text clinical reports for 529 publicly available CBCT volumes, (ii) their conversion into validated RDF/Turtle (Resource Description Framework) instances aligned with a clinician-designed OWL (Web Ontology Language) ontology spanning 13 finding types and multiple qualifier axes, and (iii) a strong baseline demonstrating the effectiveness of our setup and establishing a foundation for future work. We formulate CBCT reporting as a three-stage structured prediction problem—i.e., finding detection, anatomical slot allocation, and property prediction—and introduce a hierarchical evaluation suite of six clinically interpretable metrics that decouple detection, localization, and characterization. A baseline model using frozen multi-scale VoxTell features, a structure-indexed encoder, and ontology-driven prediction heads achieves strong results under 5-fold cross-validation, with stage-decoupled analysis identifying presence detection as the primary deployment bottleneck. Dataset, ontology, and code are publicly released: https://github.com/AImageLab-zip/CBCT-Report

Large-scale benchmarks such as BraTS have driven progress in brain tumor segmentation, but they provide only masks with limited access to the clinical semantics found in radiology reports. We introduce ReportX, a paired resource of 257 clinical reports aligned to BraTS-GLI-2023 subjects, structured into a rich set of qualitative and quantitative attributes. Qualitative fields are curated by clinicians, while quantitative descriptors are automatically derived via atlas-based localization and geometric computations. We compare our annotation schema to existing report-augmented datasets and show that ReportX provides substantially broader coverage of clinically relevant factors. To exploit this supervision, we encode reports using biomedical language models and incorporate their embeddings as auxiliary semantic guidance for 3D tumor segmentation during training. Experimental results demonstrate that the proposed vision-text alignment improves segmentation performance on standard BraTS metrics, with clinically curated reports providing more consistent improvements than automatically generated or less-structured counterparts. We publicly release the dataset (https://ditto.ing.unimore.it/reportx) and the source code (https://github.com/AImageLab-zip/ReportX).

Reproducibility policies promise "checkable" medical-imaging science, yet many submissions still ship unverifiable artifacts. Our analysis of 3722 MICCAI papers shows code-linking rising from 51.8% (2021) to 72.5% (2025), but ~13% of linked repositories are inaccessible or empty. We present paper-snitch, a reviewer-facing decision-support tool that turns these signals into an evidence-grounded report. Paper-snitch parses PDFs, resolves and sanity-checks repositories, and applies policy-aware checklists aligned with MICCAI expectations, producing a review-time verifiability score decomposed into interpretable sub-scores plus criterion-linked excerpts and artifacts reviewers can inspect. It never executes untrusted code or attempts GPU-heavy reproduction, focusing instead on bounded, verifiable checks. We compare paper-snitch on 100 randomly sampled MICCAI 2025 papers with human annotators using shared evaluation criteria, indicating that automated, bounded checks can scale reproducibility screening while keeping final decisions with reviewers.

Accurate delineation of maxillofacial anatomy in Cone-Beam Computed Tomography (CBCT) is essential for dental planning, but robust automated segmentation remains challenging, due to limited public multi-structure datasets and the high computational burden of 3D deep learning models. We present and release ToothFairy3, a large-scale CBCT benchmark that extends ToothFairy2 with 102 additional fully annotated scans and an expanded taxonomy covering 77 classes, including 32 tooth-specific pulp cavities and small neurovascular structures. ToothFairy3 comprises 582 volumes (over 40000 annotated objects), with 532 released with voxel-level labels and 50 held out for leakage-free, server-side evaluation. We also introduce U-Mamba2, an efficient U-Net-style architecture that inserts a Mamba2 state-space block at the bottleneck to capture global context with favorable computational scaling. Our proposed domain-informed training further improves the learning of maxillofacial anatomies. Across CNN, Transformer, and Mamba baselines, U-Mamba2 achieves competitive Dice/HD95 scores with lower latency and, compared with training on state-of-the-art public CBCT datasets, ToothFairy3-trained models generalize best to the hidden test set, particularly for maxillary structures.

Barcodes are a critical technology in industrial automation, logistics, and retail, enabling fast and reliable data capture. While deep learning has significantly improved barcode localization accuracy, most modern architectures remain too computationally demanding for real-time deployment on embedded systems without dedicated hardware acceleration. In this work, we present BaFaLo (Barcode Fast Localizer), an ultra-lightweight segmentation-based neural network for barcode localization. Our model is specifically optimized for real-time performance on low-power CPUs while maintaining high localization accuracy for both 1D and 2D barcodes. It features a two-branch architecture—comprising a local feature extractor and a global context module—and is tailored for low-resolution inputs to improve inference speed further. We benchmark BaFaLo against several lightweight architectures for object detection or segmentation, including YOLO Nano, Fast-SCNN, BiSeNet V2, and ContextNet, using the BarBeR dataset. BaFaLo achieves the fastest inference time among all deep-learning models tested, operating at 57.62ms per frame on a single CPU core of a Raspberry Pi 3B+. Despite its compact design, it achieves a decoding rate nearly equivalent to YOLO Nano for 1D barcodes and only 3.5 percentage points lower for 2D barcodes while being approximately nine times faster.