Publications by Federico Bolelli

Model reuse offers a solution to the challenges of segmentation in biomedical imaging, where high data annotation costs remain a major bottleneck for deep learning. However, although many pre-trained models are released through challenges, model zoos, and repositories, selecting the most suitable model for a new dataset remains difficult due to the lack of reliable model ranking methods. We introduce the first black-box-compatible framework for unsupervised and source-free ranking of semantic and instance segmentation models based on the consistency of predictions under perturbations. While ranking methods have been studied for classification and a few segmentation-related approaches exist, most target-related tasks such as transferability estimation or model validation and typically rely on labelled data, feature-space access, or specific training assumptions. In contrast, our method directly addresses the repository setting and applies to both semantic and instance segmentation, for zero-shot reuse or after unsupervised domain adaptation. We evaluate the approach across a wide range of biomedical segmentation tasks in both 2D and 3D imaging, showing that our estimated rankings strongly correlate with true target-domain model performance rankings. Code is available on GitHub: https://github.com/kreshuklab/model_ranking.

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.

CNNs and Transformer-based architectures are recently dominating the field of 3D medical segmentation. While CNNs face limitations in the local receptive field, Transformers require significant memory and data, making them less suitable for analyzing large 3D medical volumes. Consequently, fully convolutional network models like U-Net are still leading the 3D segmentation scenario. Although efforts have been made to reduce the Transformers computational complexity, such optimized models still struggle with content-based reasoning. This paper examines Mamba, a Recurrent Neural Network (RNN) based on State Space Models (SSMs), which achieves linear complexity and has outperformed Transformers in long-sequence tasks. Specifically, we assess Mamba’s performance in 3D medical segmentation using three widely recognized and commonly employed datasets and propose architectural enhancements to improve its segmentation effectiveness by mitigating the primary shortcomings of existing Mamba-based solutions.

This paper provides a detailed description of how to install, set up, and use "BarBeR" (Barcode Benchmark Repository) to reproduce the results presented in the ICPR 2024 paper "BarBeR: A Barcode Benchmarking Repository". The paper details the tests available in the repository and how the configuration parameters affect and influence experimental results.

Since their invention in 1949, barcodes have remained the preferred method for automatic data capture, playing a crucial role in supply chain management. 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 hampers 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. It offers a range of test setups and can be expanded to include any localization algorithm. 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 share the results obtained from running the benchmark on our dataset, offering valuable insights into the performance of different algorithms.

We introduce Bits2Bites, the first publicly available dataset for occlusal classification from intra-oral scans, comprising 200 paired upper and lower dental arches annotated across multiple clinically relevant dimensions (sagittal, vertical, transverse, and midline relationships). Leveraging this resource, we propose a multi-task learning benchmark that jointly predicts five occlusal traits from raw 3D point clouds using state-of-the-art point-based neural architectures. Our approach includes extensive ablation studies assessing the benefits of multi-task learning against single-task baselines, as well as the impact of automatically-predicted anatomical landmarks as input features. Results demonstrate the feasibility of directly inferring comprehensive occlusion information from unstructured 3D data, achieving promising performance across all tasks. Our entire dataset, code, and pretrained models are publicly released to foster further research in automated orthodontic diagnosis.

Whole Slide Images (WSIs) are crucial in histological diagnostics, providing high-resolution insights into cellular structures. In addition to challenges like the gigapixel scale of WSIs and the lack of pixel-level annotations, privacy restrictions further complicate their analysis. For instance, in a hospital network, different facilities need to collaborate on WSI analysis without the possibility of sharing sensitive patient data. A more practical and secure approach involves sharing models capable of continual adaptation to new data. However, without proper measures, catastrophic forgetting can occur. Traditional continual learning techniques rely on storing previous data, which violates privacy restrictions. To address this issue, this paper introduces Context Optimization Multiple Instance Learning (CooMIL), a rehearsal-free continual learning framework explicitly designed for WSI analysis. It employs a WSI-specific prompt learning procedure to adapt classification models across tasks, efficiently preventing catastrophic forgetting. Evaluated on four public WSI datasets from TCGA projects, our model significantly outperforms state-of-the-art methods within the WSI-based continual learning framework. The source code is available at https://github.com/FrancescaMiccolis/CooMIL.

Testicular ultrasound imaging is vital for assessing male infertility, with testicular inhomogeneity serving as a key biomarker. However, subjective interpretation and the scarcity of publicly available datasets pose challenges to automated classification. In this study, we explore supervised and unsupervised pretraining strategies using a ResNet-based architecture, supplemented by diffusion-based generative models to synthesize realistic ultrasound images. Our results demonstrate that pretraining significantly enhances classification performance compared to training from scratch, and synthetic data can effectively substitute real images in the pretraining process, alleviating data-sharing constraints. These methods offer promising advancements toward robust, clinically valuable automated analysis of male infertility. The source code is publicly available at https://github.com/AImageLab-zip/TesticulUS/.

Brain tumor segmentation is a crucial task in medical imaging that involves the integrated modeling of four distinct imaging modalities to identify tumor regions accurately. Unfortunately, in real-life scenarios, the full availability of such four modalities is often violated due to scanning cost, time, and patient condition. Consequently, several deep learning models have been developed to address the challenge of brain tumor segmentation under conditions of missing imaging modalities. However, the majority of these models have been evaluated using the 2018 version of the BraTS dataset, which comprises only $285$ volumes. In this study, we reproduce and extensively analyze the most relevant models using BraTS2023, which includes 1,250 volumes, thereby providing a more comprehensive and reliable comparison of their performance. Furthermore, we propose and evaluate the adoption of Mamba as an alternative fusion mechanism for brain tumor segmentation in the presence of missing modalities. Experimental results demonstrate that transformer-based architectures achieve leading performance on BraTS2023, outperforming purely convolutional models that were instead superior in BraTS2018. Meanwhile, the proposed Mamba-based architecture exhibits promising performance in comparison to state-of-the-art models, competing and even outperforming transformers. The source code of the proposed approach is publicly released alongside the benchmark developed for the evaluation: https://github.com/AImageLab-zip/IM-Fuse.

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.