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    Apresentação

    Centro de Telecomunicações e Multimédia

    A nossa visão é promover um mundo animado e sustentável onde a inteligência em rede permite uma interação ubíqua com o conteúdo sensorial. A missão é desenvolver sistemas e tecnologias avançadas para permitir comunicações de alta capacidade, eficientes e seguras, extração de conhecimento dos média e aplicações multimédia ubíquas imersivas.

    No CTM trabalhamos em quatro áreas principais de investigação: Tecnologias Óticas e Eletrónicas, Redes Sem Fios, Tecnologias Multimédia e de Comunicações, e Processamento de Informação e Reconhecimento de Padrões.

    Últimas Notícias

    INESC TEC com 5 projetos exploratórios FCT aprovados em 4 áreas de I&D

    Telecomunicações e multimédia, fotónica aplicada, software confiável e sistemas de computação avançada – são estas as quatro áreas que os investigadores do INESC TEC vão trabalhar no âmbito dos cinco projetos que foram aprovados através do Concurso de Projetos Exploratórios da Fundação para a Ciência e a Tecnologia (FCT).

    02 outubro 2024

    Inteligência Artificial

    Kick-off of the first European project led by INESC TEC in the health area

    It is called AI4Lungs; it aims to develop Artificial Intelligence (AI) tools and computational models to optimise the diagnosis and treatment of lung diseases. Through a holistic and multimodal approach, researchers will create a personalised healthcare solution for respiratory diseases. In late February, representatives of the 18 partner entities of the project (from 10 countries) met at INESC TEC to kick off the AI4Lungs project.

    01 abril 2024

    Comunicações

    Europa discute oportunidades de colaboração na área de comunicações sem fios em alta frequência

    Ambientes de propagação inteligentes, melhorias no processamento de sinal para a sexta geração de comunicações móveis e desenvolvimentos de rede e localização orientados para 6G foram alguns dos temas em debate num evento organizado pelos projetos europeus TERRAMETA, coordenado pelo INESC TEC, 6G-SHINE e TIMES, em colaboração com o RESTART-IN – um PRR Italiano.

    06 março 2024

    Inteligência Artificial

    Investigadores INESC TEC no primeiro protótipo desenvolvido em Portugal que aplica IA ao diagnóstico colorretal

    O trabalho que levou ao primeiro protótipo que aplica Inteligência Artificial (IA) ao diagnóstico colorretal totalmente desenvolvido por portugueses contou com investigadores do INESC TEC e do laboratório de Anatomia e Patologia Molecular (IMP Diagnostics). O trabalho foi publicado na reconhecida revista científica internacional npj Precision Oncology (https://www.nature.com/articles/s41698-024-00539-4).

    05 março 2024

    Investigadores do INESC TEC lideram discussão sobre comunicações sem fios e visão computacional na GLOBECOM

    Quase a completar o seu primeiro ano de duração, o projeto CONVERGE, coordenado pelo INESC TEC, já deu cartas numa das principais conferências da IEEE Communications Society, a GLOBECOM, na Malásia, com a organização de um painel. “Convergence of wireless communications and computer vision: a new paradigm created by the CONVERGE project” procurou debater as novas oportunidades e desafios potenciais que podem ser antecipados pela utilização de ferramentas que combinam rádio com visão computacional.

    23 janeiro 2024

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    Projetos Selecionados

    PFAI4_5eD

    Programa de Formação Avançada Industria 4 - 5a edição

    2024-2024

    Equipa
    Vítor Grade Tavares

    Coordenador de Centro

    Filipe André Ribeiro

    Coordenador de Centro

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    002

    Laboratórios

    Laboratório de Computação Musical e Sonora

    Laboratório de Tecnologias Óticas e Eletrónicas

    Publicações

    CTM Publicações

    Ler todas as publicações

    2025

    A survey on cell nuclei instance segmentation and classification: Leveraging context and attention

    Autores
    Nunes, JD; Montezuma, D; Oliveira, D; Pereira, T; Cardoso, JS;

    Publicação
    MEDICAL IMAGE ANALYSIS

    Abstract
    Nuclear-derived morphological features and biomarkers provide relevant insights regarding the tumour microenvironment, while also allowing diagnosis and prognosis in specific cancer types. However, manually annotating nuclei from the gigapixel Haematoxylin and Eosin (H&E)-stained Whole Slide Images (WSIs) is a laborious and costly task, meaning automated algorithms for cell nuclei instance segmentation and classification could alleviate the workload of pathologists and clinical researchers and at the same time facilitate the automatic extraction of clinically interpretable features for artificial intelligence (AI) tools. But due to high intra- and inter-class variability of nuclei morphological and chromatic features, as well as H&Estains susceptibility to artefacts, state-of-the-art algorithms cannot correctly detect and classify instances with the necessary performance. In this work, we hypothesize context and attention inductive biases in artificial neural networks (ANNs) could increase the performance and generalization of algorithms for cell nuclei instance segmentation and classification. To understand the advantages, use-cases, and limitations of context and attention-based mechanisms in instance segmentation and classification, we start by reviewing works in computer vision and medical imaging. We then conduct a thorough survey on context and attention methods for cell nuclei instance segmentation and classification from H&E-stained microscopy imaging, while providing a comprehensive discussion of the challenges being tackled with context and attention. Besides, we illustrate some limitations of current approaches and present ideas for future research. As a case study, we extend both a general (Mask-RCNN) and a customized (HoVer-Net) instance segmentation and classification methods with context- and attention-based mechanisms and perform a comparative analysis on a multicentre dataset for colon nuclei identification and counting. Although pathologists rely on context at multiple levels while paying attention to specific Regions of Interest (RoIs) when analysing and annotating WSIs, our findings suggest translating that domain knowledge into algorithm design is no trivial task, but to fully exploit these mechanisms in ANNs, the scientific understanding of these methods should first be addressed.

    FecharLer Abstract

    2025

    Causal representation learning through higher-level information extraction

    Autores
    Silva, F; Oliveira, HP; Pereira, T;

    Publicação
    ACM COMPUTING SURVEYS

    Abstract
    The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning, and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science, and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.

    FecharLer Abstract

    2025

    Evaluation of Lyrics Extraction from Folk Music Sheets Using Vision Language Models (VLMs)

    Autores
    Sales Mendes, A; Lozano Murciego, Á; Silva, LA; Jiménez Bravo, M; Navarro Cáceres, M; Bernardes, G;

    Publicação
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

    Abstract
    Monodic folk music has traditionally been preserved in physical documents. It constitutes a vast archive that needs to be digitized to facilitate comprehensive analysis using AI techniques. A critical component of music score digitization is the transcription of lyrics, an extensively researched process in Optical Character Recognition (OCR) and document layout analysis. These fields typically require the development of specific models that operate in several stages: first, to detect the bounding boxes of specific texts, then to identify the language, and finally, to recognize the characters. Recent advances in vision language models (VLMs) have introduced multimodal capabilities, such as processing images and text, which are competitive with traditional OCR methods. This paper proposes an end-to-end system for extracting lyrics from images of handwritten musical scores. We aim to evaluate the performance of two state-of-the-art VLMs to determine whether they can eliminate the need to develop specialized text recognition and OCR models for this task. The results of the study, obtained from a dataset in a real-world application environment, are presented along with promising new research directions in the field. This progress contributes to preserving cultural heritage and opens up new possibilities for global analysis and research in folk music. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

    FecharLer Abstract

    2025

    Model compression techniques in biometrics applications: A survey

    Autores
    Caldeira, E; Neto, PC; Huber, M; Damer, N; Sequeira, AF;

    Publicação
    INFORMATION FUSION

    Abstract
    The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing level of complexity, limiting their usefulness in human-oriented applications, which are usually deployed in resource-constrained devices. This led to the development of compression techniques that drastically reduce the computational and memory costs of deep learning models without significant performance degradation. These compressed models are especially essential when implementing multi-model fusion solutions where multiple models are required to operate simultaneously. This paper aims to systematize the current literature on this topic by presenting a comprehensive survey of model compression techniques in biometrics applications, namely quantization, knowledge distillation and pruning. We conduct a critical analysis of the comparative value of these techniques, focusing on their advantages and disadvantages and presenting suggestions for future work directions that can potentially improve the current methods. Additionally, we discuss and analyze the link between model bias and model compression, highlighting the need to direct compression research toward model fairness in future works.

    FecharLer Abstract

    2024

    Incremental Redundancy HARQ Communication Schemes applied to Energy Efficient IoT Systems

    Autores
    Silva, SM; Almeida, NT;

    Publicação
    2024 IEEE 22ND MEDITERRANEAN ELECTROTECHNICAL CONFERENCE, MELECON 2024

    Abstract
    The rapid proliferation of Internet of Things (IoT) systems, encompassing a wide range of devices and sensors with limited battery life, has highlighted the critical need for energy-efficient solutions to extend the operational lifespan of these battery-powered devices. One effective strategy for reducing energy consumption is minimizing the number and size of retransmitted packets in case of communication errors. Among the potential solutions, Incremental Redundancy Hybrid Automatic Repeat reQuest (IR-HARQ) communication schemes have emerged as particularly compelling options by adopting the best aspects of error control, namely, automatic repetition and variable redundancy. This work addresses the challenge by developing a simulator capable of executing and analysing several (H)ARQ schemes using different channel models, such as the Additive White Gaussian Noise (AWGN) and Gilbert-Elliott (GE) models. The primary objective is to compare their performance across multiple metrics, enabling a thorough evaluation of their capabilities. The results indicate that IR-HARQ outperforms alternative methods, especially in the presence of burst errors. Furthermore, its potential for further adaptation and enhancement opens up new ways for optimizing energy consumption and extending the lifespan of battery-powered IoT devices.

    FecharLer Abstract

    Factos & Números

    82Investigadores

    2016

    28Investigadores Séniores

    2016

    15Docentes do Ensino Superior

    2020

    Ver todos os Factos & Números
    Contactos
    Filipe André Ribeiro

    Coordenador

    +351222094299

    filipe.a.ribeiro@inesctec.pt