1,721,033 research outputs found
Classification of Histologic Images Using a Single Staining: Experiments with Deep Learning on Deconvolved Images
No full textThe automated analysis of digitized immunohistochemistry microscope slides is usually a challenging task, because markers should be analysed on the tumor area only. Tumor areas could be recognized on a different slide, stained with Haematoxylin-Eosin. The basic idea of the present poster is to evaluate how well deep learning methods perform on the single haematoxylin component of staining, with the prospective possibility of developing a classifier able to recognize tumor areas on IHC slides on their haematoxylin component only. In a preliminary experiment, single stain images obtained by H-E color deconvolution showed an accuracy of 0.808 and 0.812 for Hematoxilyn and Eosin components, respectively
Graph Neural Networks for Gleason Grading in Prostate Histopathology Images
Full text linkProstate cancer is a leading cause of cancer-related deaths, with Gleason grading being key for assessing tumor aggressiveness. We propose a Graph Neural Network-based approach to automate Gleason grading using the Automated Gleason Grading Challenge 2022 dataset. Patch-level graphs constructed from Hematoxylin and Eosin-stained Whole-Slide Images were classified into Gleason grades. Our results show that Graph Neural Networks, specifically Graph Attention Networks and Graph Convolutional Networks, effectively distinguish between grades despite class imbalance. Focal Loss improves the classification of the minority Gleason Grade 5, which is crucial for detecting aggressive prostate cancer. Our models outperform state-of-the-art methods, achieving higher F1-scores without scanner generalization techniques
Ontological modeling of the International Classification of Functioning, Disabilities and Health (ICF): Activities&Participation and Environmental Factors components
Full text linkBackground: The International Classification of Functioning, Disability and Health (ICF) is a classification of health and health-related states developed by the World Health Organization (WHO) to provide a standard and unified language to be used as a reference model for the description of health and health-related states. The concept of functioning on which ICF is based is that of a “dynamic interaction between a person’s health condition, environmental factors and personal factors”. This overall model has been translated into a classification covering all the main components of functioning. However, the practical use of ICF has highlighted some formal problems, mainly concerning conceptual clarity and ontological coherence. Methods: In the present work, we propose an initial ontological formalization of ICF beyond its current status, focusing specifically on the interaction between activities and participation and environmental factors. The formalization has been based on ontology engineering methods to drive goal and scope definition, knowledge acquisition, selection of an upper ontology for mapping, conceptual model definition and evaluation, and finally representation using the Ontology Web Language (OWL). Results: A conceptual model has been defined in a graphical language that included 202 entities, when possible mapped to the SUMO upper ontology. The conceptual model has been validated against 60 case studies from the literature, plus 6 ad-hoc case studies. The model has been then represented using OWL. Conclusions: This formalization might provide the basis for a revision of the ICF classification in line with current efforts made by WHO on the International Classification of Diseases and on the International Classification of Health Interventions
Leveraging LLMs for Energy Forecasting: The AcegasApsAmga Case Study
No full textThis paper presents AcegasApsAmga’s application of Large Language Models (LLMs) for energy forecasting, focusing on both short-term and long-term power consumption predictions. We detail the model adaptation process, including fine-tuning techniques specific to energy data, and the integration of temporal and contextual features using Retrieval Augmented Generation (RAG) to enhance forecasting accuracy
The economics of telepathology: a case study
No full textThere are several obstacles that slow down the diffusion of telepathology. One is related to uncertainty about the economic consequences of its adoption, possibly more so than in other fields of telemedicine. We have evaluated the economics of telepathology when used to provide a frozen-section service to a mountain hospital, in comparison with three current alternatives. In the specific situations studied, no one model was always less expensive than the others. In particular, owing to the very low cost of the ambulance service provided by the Red Cross, the ambulance model was least expensive when dealing with up to 73 frozen sections a year, while at higher case-loads telepathology was cheaper. If ambulance transfer is neglected, telepathology appears to be the most convenient approach to the remote frozen-section service. Although the consultant pathologist costs more than telemedicine, during free time he/she could perform other (routine) work, thus reducing the real cost of frozen sections
Explainable Classification of Medical Documents Through a Text-to-Text Transformer
No full textDeath certificates are important medical records which are collected for the purpose of public healthcare and statistics by multiple organizations around the globe. Due to their importance, those certificates are compiled by experienced medical practitioner according to a standard defined by the World Health Organization including rules to select an underlying cause of death (UCOD). For this reason, the coding of death certificates is a slow and costly process. To overcome these issues, the scientific community proposed deep learning approaches to perform such a task. Despite those systems achieve high accuracy scores (close to 1), their complexity makes the obscure to the final user, making it unfeasible the adoption as a decision support system. In this paper, we propose a model based on text-to-text transformers which is able to provide a UCOD as well as to generate a human-readable explanation for its classification. We compare the proposed approach to state-of-the-art interpretable rule-based systems
From EHR to Machine Learning: A Preliminary Report on an Ingestion Pipeline Based on JSON-LD
Full text linkIn this paper, we present the preliminary experiments for the development of an ingestion mechanism to move data from Electronic Health Records to machine learning processes, based on the concept of Linked Data and the JSON-LD format
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