Scientific seminars given by experts working in industrial sectors working on topics such as biomedical imaging, cultural heritage, HPC, medical imaging.


CINECA Bologna: Antonella Guidazzoli “AI and Cultural Heritage. Between Research and Creativity”

Abstract. This talk will present some case studies on using artificial intelligence (AI) to preserve , study and valorize  cultural heritage digital assets. At the heart of the approach is recognizing the need for a human AI frameworks in which data scientists and  domain experts collaborate synergistically.  Moreover promoting interdisciplinary partnerships is essential touphold ethical principles and serve the public good.



ESAOTE Genova: Giulia Pinto “Magnetic Resonance Imaging: how to speed up image acquisition”

Abstract. Over the years since 1970, Magnetic Resonance Imaging (MRI) has developed into as one of the preferred choices for many radiological exams today. It relies primarily on its ability to detect water, which constitutes a significant portion of most tissues (around 70-90%). Changes in the water content and properties within tissues due to diseases or injuries can be substantial, rendering MRI highly effective in diagnosis due to its sensitivity. ESAOTE specializes in designing MRI systems with low-field technology (0.25 T to 0.4 T), offering several advantages including improved patient comfort, cost reduction, less demanding installation requirements, and lower energy consumption. However, the trade-off for using low-field MRI is a decrease in signal strength, often requiring longer scan times to achieve high-quality diagnostic images. Hence, there is a need for techniques to accelerate image acquisition. Specifically, ESAOTE has developed the Speed-Up technique, inspired to compressed-sensing techniques. Recent advances in collaboration with the Amsterdam University Medical Center, within the RAISE SPOKE 2 project, aim to improve the reconstruction algorithm using artificial intelligence (AI), while maintaining the diagnostic accuracy of traditional, longer scans. This was achieved by optimizing the k-space undersampling scheme and reconstructions, using the Cascades of Independently Recurrent Inference Machines (CIRIM). Promising image quality was observed up to an acceleration factor of at least 2.5.
Keywords: Low-field MRI, MRI acceleration methods, MRI reconstruction
Acknowledgements: Funded by the European Union - NextGenerationEU and by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), Mission 4, Component 2, Investment 1.5, project “RAISE - Robotics and AI for Socio-economic Empowerment” (ECS00000035).



DataLogic Bologna: Angelo Carraggi “Efficient Unsupervised Anomaly detection”

Abstract. Anomaly detection is a primary need for industrial/manufacturing applications and Datalogic business, but many challenges persist. The collection and annotation of data is expensive and most of the time unpractical as the deployed systems are usually not remotely accessible. The image resolution and the frame rate require computing-intensive algorithms that often do not fit the real-time constraint on embedded devices. In this study, we propose a novel solution that, inspired by recent advancements in this field obtained by normalizing-flow and patch-based feature distribution, combines unsupervised learning with efficient processing to deploy an optimized solution to reach a high classification accuracy while working with few training samples.



IIT Genova:  Giuseppe Vicidomini “Image Scanning Microscopy: Single-Photon Array Detectors Meet Machine Learning for Super-Resolution Imaging”

Abstract. Confocal laser-scanning microscopy (CLSM) has long been celebrated in life-science research for its unique blend of spatial and temporal resolution, coupled with its versatile applications. However, recent advancements in detector technology have sparked a transformative shift in CLSM, triggered by the introduction of novel single-photon array detectors. These detectors, poised to supplant single-element detectors (also known as bucket detectors), offer access to previously discarded sample information, reshaping the trajectory of CLSM.

In traditional CLSM, images are generated by raster scanning a focused laser beam across the sample, with single-element detectors registering a single-intensity value at each sample position. In contrast, single-photon array detectors capture true temporal images at each scanning position, transitioning CLSM into image scanning microscopy (ISM). Image scanning microscopy transcends traditional CLSM by generating not merely a two-dimensional dataset but a five-dimensional one, incorporating four spatial dimensions and a temporal dimension. This enables the reconstruction of highly informative and super-resolved images of the sample.

This seminar will delve into the foundational principles of ISM, starting with the formulation of the forward model underlying the technique. Subsequently, a maximum likelihood approach, considering Poissonian noise, will be presented for reconstructing super-resolved images from the four-dimensional spatial dataset. An extension of this framework will incorporate the temporal dimension, enabling the reconstruction of fluorescence lifetime images that integrate structural and functional sample information.

Furthermore, the seminar will explore leveraging the ISM dataset and deep learning techniques to accurately estimate the point-spread function of the optical system. This has the potential to significantly enhance the quality of reconstructed super-resolved images.

By elucidating these advancements and future prospects, this seminar aims to inspire researchers to harness the full potential of ISM in pushing the boundaries of biomedical imaging.



Matthieu Terris, Dongdong Chen and Julian Tachella: "DeepInverse,  A hands-on tutorial for solving imaging inverse problems with the deepinverse library "

Abstract. In this tutorial, we will explore different approaches for solving imaging inverse problems using deep learning, such as plug-and-play and diffusion methods, unrolled architectures and self-supervised learning approaches. The algorithms will be illustrated using the deepinverse library ( We will provide jupyter notebook examples, and the participants will be invited to try out and modify the reconstruction methods and explore different imaging modalities. We will also discuss how to contribute and participate in the deepinverse open source project.