Publications by Ettore Candeloro

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.

Progress in dental computer vision is limited by the absence of large-scale multimodal datasets that jointly capture 3D intraoral geometry and 2D appearance across diverse clinical settings. Existing resources are typically unimodal, which hinders robust cross-modal learning and generalization. We assemble and release a multi-center dataset of 1,000 patients comprising 2,000 registered upper/lower intraoral scans, 5,000 paired intraoral photographs, and 2,403 clinician-authored reports. This combination links detailed 3D dental geometry with complementary 2D evidence, supporting occlusal and orthodontic analysis. Moreover, to enable scalable and privacy-preserving acquisition and annotation across distributed centers, we introduce an open platform that supports multimodal ingestion and structured labeling. Experiments indicate that state-of-the-art multimodal models fail to generate clinically faithful reports, motivating geometry-aware adaptation. We therefore propose IOS-Qwen, which fuses a PointTransformer 3D encoder with Qwen3-VL to generate structured, point-cloud-conditioned reports. Together, the dataset, the platform, and the baselines establish a foundation for multimodal dental AI research. Code is publicly released (https://github.com/AImageLab-zip/IOS-Report)

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.

Convolutional Neural Networks (CNNs) have been broadly employed in dermoscopic image analysis, mainly due to the large amount of data gathered by the International Skin Imaging Collaboration (ISIC). But where do neural networks look? Several authors have claimed that the ISIC dataset is affected by strong biases, i.e. spurious correlations between samples that machine learning models unfairly exploit while discarding the useful patterns they are expected to learn. These strong claims have been supported by showing that deep learning models maintain excellent performance even when "no information about the lesion remains" in the debased input images. With this paper, we explore the interpretability of CNNs in dermoscopic image analysis by analyzing which characteristics are considered by autonomous classification algorithms. Starting from a standard setting, experiments presented in this paper gradually conceal well-known crucial dermoscopic features and thoroughly investigate how CNNs performance subsequently evolves. Experimental results carried out on two well-known CNNs, EfficientNet-B3, and ResNet-152, demonstrate that neural networks autonomously learn to extract features that are notoriously important for melanoma detection. Even when some of such features are removed, the others are still enough to achieve satisfactory classification performance. Obtained results demonstrate that literature claims on biases are not supported by carried-out experiments. Finally, to demonstrate the generalization capabilities of state-of-the-art CNN models for skin lesion classification, a large private dataset has been employed as an additional test set.