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11th European Conference on Precision Livestock Farming

作者： Mayrhuber, E. Oczak, M. Iwersen, M. Brunner, D. Dorfer, V. Winkler, S.M. Bioinformatics Research Group PLFDoc University of Applied Sciences Upper Austria Hagenberg Austria Computer Vision Lab TU Wien Vienna Austria Precision Livestock Farming Hub The University of Veterinary Medicine Vienna Vienna Austria Center for Animal Nutrition and Welfare The University of Veterinary Medicine Vienna Vienna Austria Center for Veterinary Systems Transformation and Sustainability Clinical Unit for Herd Health Management The University of Veterinary Medicine Vienna Vienna Austria

ISBN: (纸本)9791221067361

The ability to predict farrowing time in pigs is crucial for optimal farm management and ensuring piglet survival. This study investigates the potential of explainable artificial intelligence for precision livestock farming, focusing on acceleration sensor data from sows around the time of farrowing. Accurately predicting farrowing allows farmers to balance both, piglet safety by avoiding piglet crushing events and the mother sow's welfare, by avoiding early crating. Traditional machine learning models often lack interpretability ("closed-box" problem). Explainable artificial intelligence offers a solution by revealing the decision-making processes within the models. This work is split into two stages. First the detection of nest building behaviour and second the prediction of the remaining time to farrowing. The nest building detection was possible for 166 out of 180 sows and successful within 48 hours before farrowing for 149 sows. The nest building detection is purely based on the change of a sow's activity level and uses no machine learning techniques. Further, this study utilizes symbolic regression, an open-box modelling technique that generates interpretable mathematical formulas, to predict the remaining time until the onset of farrowing. The results are compared with closed-box models, like support vector regression and artificial neural networks, for both explainability and performance. Notably, symbolic regression achieved a mean absolute error of 9.28 hours for the time-to-farrowing prediction, outperforming the closed-box models in terms of performance and interpretability. This research demonstrates that explainable artificial intelligence, specifically symbolic regression, can offer both interpretable results and competitive performance in precision livestock farming. © 2024 11th European Conference on Precision Livestock Farming. All rights reserved.

关键词： Support vector regression

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Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin

arXiv

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arXiv 2025年

作者： Guarrasi, Valerio Di Feola, Francesco Restivo, Rebecca Tronchin, Lorenzo Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Computing Science Umeå University Umeå Sweden Department of Diagnostics and Intervention Biomedical Engineering and Radiation Physics Umeå University Umeå Sweden

Generating positron emission tomography (PET) images from computed tomography (CT) scans via deep learning offers a promising pathway to reduce radiation exposure and costs associated with PET imaging, improving patient care and accessibility to functional imaging. Whole-body image translation presents challenges due to anatomical heterogeneity, often limiting generalized models. We propose a framework that segments whole-body CT images into four regions—head, trunk, arms, and legs—and uses district-specific Generative Adversarial Networks (GANs) for tailored CT-to-PET translation. Synthetic PET images from each region are stitched together to reconstruct the whole-body scan. Comparisons with a baseline non-segmented GAN and experiments with Pix2Pix and CycleGAN architectures tested paired and unpaired scenarios. Quantitative evaluations at district, whole-body, and lesion levels demonstrated significant improvements with our district-specific GANs. Pix2Pix yielded superior metrics, ensuring precise, high-quality image synthesis. By addressing anatomical heterogeneity, this approach achieves state-of-the-art results in whole-body CT-to-PET translation. This methodology supports healthcare Digital Twins by enabling accurate virtual PET scans from CT data, creating virtual imaging representations to monitor, predict, and optimize health outcomes. © 2025, CC BY-SA.

关键词： Positron emission tomography

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Optimal Brain Dissection in Dense Autoencoders: Towards Determining Feature Importance in -Omics Data 5

Optimal Brain Dissection in Dense Autoencoders: Towards Dete...

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5th IEEE International Conference on BioInspired Processing, BIP 2023

作者： Amin, Fin Van Den Broeck, Lisa De Smet, Ive Locke, Anna M. Sozzani, Rossangela North Carolina State University Department of Electrical and Computer Engineering Raleigh United States Protealis NV Ghent Belgium Ghent University VIB Center for Plant Systems Biology Department of Plant Biotechnology and Bioinformatics GhentB-9052 Belgium Soybean Nitrogen Fixation Research Unit USDA-ARS Raleigh United States North Carolina State University NC Plant Sciences Initiative Raleigh United States

ISBN: (纸本)9798350330052

Recently, there has been increased interest in ma-chine learning explainability. Understanding the complex relationship between input features of a model and their respective outputs is of increased relevance, especially in biological science. In this paper, we introduce Optimal Brain Dissection (OBD), an innovative methodology designed to examine the importance of first-layer connections in a biology-inspired autoencoder. We incorporated regulator-target interactions within the first autoencoder layer, representing biological regulatory networks, and identified their importance to the reconstruction error, a critical aspect in navigating the complexity of high-dimensional omics data. Through a combination of pruning techniques and counterfactual reasoning, OBD offers a method to quantify feature importance, factoring in both weight magnitude and time-to-laziness. To implement this method, we propose a Dense Autoencoder (DAE) architecture, aiming for increased efficiency and reduced computation. Tailored for omics data, the DAE employs skip concatenations and circumvents non-existent target-target interactions. Our approach aims to understand the relative importance of connections for autoencoder performance, a critical step towards better counter-factual reasoning for neural networks. © 2023 IEEE.

关键词： au-toencoder bioinformatics counter-factual reasoning feature importance ML explainability omics pruning

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A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical Applications

arXiv

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arXiv 2024年

作者： Guarrasi, Valerio Aksu, Fatih Caruso, Camillo Maria Feola, Francesco Di Rofena, Aurora Ruffini, Filippo Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Biomedical Sciences Humanitas University Milan Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Deep learning has revolutionized biomedical research by providing sophisticated methods to handle complex, high-dimensional data. Multimodal deep learning (MDL) further enhances this capability by integrating diverse data types such as imaging, textual data, and genetic information, leading to more robust and accurate predictive models. In MDL, differently from early and late fusion methods, intermediate fusion stands out for its ability to effectively combine modality-specific features during the learning process. This systematic review aims to comprehensively analyze and formalize current intermediate fusion methods in biomedical applications. We investigate the techniques employed, the challenges faced, and potential future directions for advancing intermediate fusion methods. Additionally, we introduce a structured notation to enhance the understanding and application of these methods beyond the biomedical domain. Our findings are intended to support researchers, healthcare professionals, and the broader deep learning community in developing more sophisticated and insightful multimodal models. Through this review, we aim to provide a foundational framework for future research and practical applications in the dynamic field of MDL. © 2024, CC BY-NC-SA.

关键词： Data fusion

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From Pre-Training to Fine-Tuning: An In-Depth Analysis of Large Language Models in the Biomedical Domain

SSRN

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SSRN 2024年

作者： Bonfigli, Agnese Bacco, Luca Merone, Mario Dell’Orletta, Felice ItaliaNLP Lab Institute of Computational Linguistics "Antonio Zampolli" National Research Council Via Giuseppe Moruzzi 1 Pisa56124 Italy Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Via Alvaro del Portillo 21 Rome00128 Italy

In this study, we delve into the adaptation and effectiveness of Transformer-based, pre-trained Large Language Models (LLMs) within the biomedical domain, a field that poses unique challenges due to its complexity and the specialized nature of its data. Building on the foundation laid by the transformative architecture of Transformers, we investigate the nuanced dynamics of LLMs through a multifaceted lens, focusing on two domain-specific tasks, i.e., Natural Language Inference (NLI) and Named Entity Recognition (NER).Our objective is to bridge the knowledge gap regarding how these models’ downstream performances correlate with their capacity to encapsulate task-relevant *** achieve this goal, we probed and analyzed the inner encoding and attention mechanisms in LLMs, both encoder- and decoder-based, tailored for either general or biomedical-specific applications. This examination occurs before and after the models are fine-tuned across various data volumes. Our findings reveal that the models' downstream effectiveness is intricately linked to specific patterns within their internal mechanisms, shedding light on the nuanced ways in which LLMs process and apply knowledge in the biomedical context. The source code for this paper is available at https://***/agnesebonfigli99/LLMs-in-the-Biomedical-Domain. © 2024, The Authors. All rights reserved.

关键词： Signal encoding

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BodySLAM: A Generalized Monocular Visual SLAM Framework for Surgical Applications

arXiv

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arXiv 2024年

作者： Manni, Guido Lauretti, Clemente Prata, Francesco Papalia, Rocco Zollo, Loredana Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Unit of Advanced Robotics and Human-Centred Technologies Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Department of Urology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy

Endoscopic surgery relies on two-dimensional views, posing challenges for surgeons in depth perception and instrument manipulation. While Monocular Visual Simultaneous Localization and Mapping (MVSLAM) has emerged as a promising solution, its implementation in endoscopic procedures faces significant challenges due to hardware limitations, such as the use of a monocular camera and the absence of odometry sensors. This study presents BodySLAM, a robust deep learning-based MVSLAM approach that addresses these challenges through three key components: CycleVO, a novel unsupervised monocular pose estimation module;the integration of the state-of-the-art Zoe architecture for monocular depth estimation;and a 3D reconstruction module creating a coherent surgical map. The approach is rigorously evaluated using three publicly available datasets (Hamlyn, EndoSLAM, and SCARED) spanning laparoscopy, gastroscopy, and colonoscopy scenarios, and benchmarked against four state-of-the-art methods. Results demonstrate that CycleVO exhibited competitive performance with the lowest inference time among pose estimation methods, while maintaining robust generalization capabilities, whereas Zoe significantly outperformed existing algorithms for depth estimation in endoscopy. BodySLAM’s strong performance across diverse endoscopic scenarios demonstrates its potential as a viable MVSLAM solution for endoscopic applications. Copyright © 2024, The Authors. All rights reserved.

关键词： Depth perception

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Postural Data Analysis using AI-powered Classification Models

Postural Data Analysis using AI-powered Classification Model...

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Workshop on Metrology for Industry 4.0 and IoT

作者： Luca Bacco Matteo Coletta Martina Zaltieri Daniela Lo Presti Carlo Massaroni Emiliano Schena Mario Merone Department of Engineering Research Unit of Computer Systems and Bioinformatics Università Campus Bio-Medico di RomaVia Alvaro del Portillo 21 Rome Italy Department of Engineering Research Unit of Measurements and Biomedical Instrumentation Università Campus Bio-Medico di RomaVia Alvaro del Portillo 21 Rome Italy

Nowadays, more and more people are working remotely or in professions that require them to sit for long periods of time. Unfortunately, spending too much time in a seated position can lead to a range of physical and mental health problems, such as musculoskeletal discomfort, headaches, and respiratory issues. These problems are often exacerbated by poor posture, which is common when sitting for extended periods of time. To address this issue, we have developed a system for classifying sitting postures using sensors and machine learning algorithms, achieving 100% of accuracy with a set of seven fiber Bragg grating sensors. We have further optimized the multisensor system by studying the optimal number of sensors and their positioning on the spine, achieving over 95% accuracy in classifying upright, kyphotic, and lordotic positions with as little as only two devices.

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Class Balancing Diversity Multimodal Ensemble for Alzheimer’s Disease Diagnosis and Early Detection

arXiv

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arXiv 2024年

作者： Francesconi, Arianna di Biase, Lazzaro Cappetta, Donato Rebecchi, Fabio Soda, Paolo Sicilia, Rosa Guarrasi, Valerio Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Operative Research Unit of Neurology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Eustema S.p.A. Research and Development Centre Naples Italy Department of Diagnostic and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Alzheimer’s disease (AD) poses significant global health challenges due to its increasing prevalence and associated societal costs. Early detection and diagnosis of AD are critical for delaying progression and improving patient outcomes. Traditional diagnostic methods and single-modality data often fall short in identifying early-stage AD and distinguishing it from Mild Cognitive Impairment (MCI). This study addresses these challenges by introducing a novel approach: multImodal enseMble via class BALancing diversity for iMbalancEd Data (IMBALMED). IMBALMED integrates multimodal data from the Alzheimer’s Disease Neuroimaging Initiative database, including clinical assessments, neuroimaging phenotypes, biospecimen and subject characteristics data. It employs an ensemble of model classifiers, each trained with different class balancing techniques, to overcome class imbalance and enhance model accuracy. We evaluate IMBALMED on two diagnostic tasks (binary and ternary classification) and four binary early detection tasks (at 12, 24, 36, and 48 months), comparing its performance with state-of-the-art algorithms and an unbalanced dataset method. IMBALMED demonstrates superior diagnostic accuracy and predictive performance in both binary and ternary classification tasks, significantly improving early detection of MCI at 48-month time point. The method shows improved classification performance and robustness, offering a promising solution for early detection and management of AD. © 2024, CC BY-NC-ND.

关键词： Adversarial machine learning

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A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Values

arXiv

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arXiv 2023年

作者： Caruso, Camillo Maria Guarrasi, Valerio Ramella, Sara Soda, Paolo Research Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy Operative Research Unit of Radiation Oncology Fondazione Policlinico Universitario Campus Bio-Medico Rome Italy Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Background and Objective: In the field of lung cancer research, particularly in the analysis of overall survival (OS), artificial intelligence (AI) serves crucial roles with specific aims. Given the prevalent issue of missing data in the medical domain, our primary objective is to develop an AI model capable of dynamically handling this missing data. Additionally, we aim to leverage all accessible data, effectively analyzing both uncensored patients who have experienced the event of interest and censored patients who have not, by embedding a specialized technique within our AI model, not commonly utilized in other AI tasks. Through the realization of these objectives, our model aims to provide precise OS predictions for non-small cell lung cancer (NSCLC) patients, thus overcoming these significant challenges. Methods: We present a novel approach to survival analysis with missing values in the context of NSCLC, which exploits the strengths of the transformer architecture to account only for available features without requiring any imputation strategy. More specifically, this model tailors the transformer architecture to tabular data by adapting its feature embedding and masked self-attention to mask missing data and fully exploit the available ones. By making use of ad-hoc designed losses for OS, it is able to account for both censored and uncensored patients, as well as changes in risks over time. Results: We compared our method with state-of-the-art models for survival analysis coupled with different imputation strategies. We evaluated the results obtained over a period of 6 years using different time granularities obtaining a Ct-index, a time-dependent variant of the C-index, of 71.97, 77.58 and 80.72 for time units of 1 month, 1 year and 2 years, respectively, outperforming all state-of-the-art methods regardless of the imputation method used. Conclusions: The results show that our model not only outperforms the state-of-the-art’s performance but also simplifies

关键词： Lung cancer

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Early Experiences on using Triplet Networks for Histological Subtype Classification in Non-Small Cell Lung Cancer

Early Experiences on using Triplet Networks for Histological...

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Annual IEEE Symposium on computer-Based Medical systems

作者： Fatih Aksu Fabrizia Gelardi Arturo Chiti Paolo Soda Department of Biomedical Sciences Humanitas University Milan Italy IRCCS Humanitas Research Hospital Milan Italy Research Unit of Computer Systems and Bioinformatics Campus Bio-Medico University of Rome Rome Italy Department of Radiation Sciences Radiation Physics Biomedical Engineering Umeå University Umeå Sweden

Lung cancer has the highest mortality rate among tumours and an accurate pathological assessment is crucial to deliver personalized treatments to patients. The gold standard for pathological assessment requires invasive procedures, which are not always possible and might cause clinical complications. Therefore, in the last years, efforts have been directed towards the development of machine and deep learning approaches for virtual biopsy, which leverage routinely collected CT scans. However, in many cases, the available datasets are limited in size, an issue that limits the training of any model. In this paper, we investigate if triplet networks can cope with this limitation: they are a class of neural networks that uses the same weights while working in tandem on three different input vectors to minimize the loss function. In particular, on a dataset including 87 CT scans collected from patients suffering from non-small cell lung cancer, we experimentally compare triplet networks against plain deep networks when performing histological subtype classification. The results show that the former outperforms the latter in almost all experiments.

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