Cookies Policy
The website need some cookies and similar means to function. If you permit us, we will use those means to collect data on your visits for aggregated statistics to improve our service. Find out More
Accept Reject
  • Menu
    Facts & Numbers
    000
    Presentation

    Telecommunications and Multimedia

    At CTM, our vision is to promote a lively and sustainable world where networked intelligence enables ubiquitous interaction with sensory-rich content. Our mission is to develop advanced systems and technologies to enable high capacity, efficient, and secure communications, media knowledge extraction, and immersive ubiquitous multimedia applications.

    We work in 4 main areas of research: Optical and Electronic Technologies, Wireless Networks, Multimedia and Communications Technologies, and VCMI (Visual Computing and Machine Intelligence).

    Latest News

    INESC TEC with five FCT exploratory projects approved in four R&D areas

    Telecommunications and Multimedia, Applied Photonics, High-assurance Software and Advanced Computing Systems – these are the four domains that INESC TEC researchers will explore within the scope of the five projects that were approved through the Call for Exploratory Projects promoted by the Foundation for Science and Technology (FCT).

    02nd October 2024

    Artificial Intelligence

    Já arrancou o primeiro projeto europeu liderado pelo INESC TEC na área da saúde

    Chama-se AI4Lungs e tem como objetivo desenvolver ferramentas e modelos computacionais baseados em Inteligência Artificial para otimizar o diagnóstico e o tratamento de doenças pulmonares. Através de uma abordagem holística e multimodal, os investigadores vão criar uma solução de cuidados de saúde personalizados para doenças respiratórias. No final de fevereiro, representantes das 18 entidades parceiras do projeto, provenientes de 10 países, reuniram-se no INESC TEC para assinalar o arranque do AI4Lungs.

    01st April 2024

    Communications

    Europe discusses collaboration opportunities in high-frequency wireless communications

    Smart propagation environments, improvements in signal processing for the sixth generation of mobile communications, and 6G-centred network and location developments were some of the topics discussed at an event organised by the European projects TERRAMETA (coordinated by INESC TEC), 6G-SHINE and TIMES, in collaboration with RESTART-IN – an Italian PRR.

    06th March 2024

    Artificial Intelligence

    INESC TEC researchers work on the first prototype that applies AI to colorectal diagnosis developed in Portugal

    The work behind the first prototype that uses Artificial Intelligence (AI) for colorectal diagnosis was fully developed by Portuguese researchers INESC TEC, and the IMP Diagnostics Molecular & Anatomic Pathology laboratory; the work featured in the renowned international scientific journal npj Precision Oncology (https://www.nature.com/articles/s41698-024-00539-4 ).

    05th March 2024

    INESC TEC researchers led discussion on wireless communications and computer vision at GLOBECOM

    After almost one year, the CONVERGE project (coordinated by INESC TEC) has already showed relevant outcomes at one of the main conferences of the IEEE Communications Society, the GLOBECOM (Malaysia) – namely, through the organisation of a panel. “Convergence of wireless communications and computer vision: a new paradigm created by the CONVERGE project” sought to discuss the new opportunities and potential challenges associated with the use of tools that combine radio with computer vision.

    23rd January 2024

    View all News
    001

    Featured Projects

    PFAI4_5eD

    Programa de Formação Avançada Industria 4 - 5a edição

    2024-2024

    Team
    Vítor Grade Tavares

    Centre Coordinator

    Filipe André Ribeiro

    Centre Coordinator

    ViewClose Alphabet
    • a
    • b
    • c
    • d
    • e
    • f
    • g
    • h
    • i
    • j
    • k
    • l
    • m
    • n
    • o
    • p
    • q
    • r
    • s
    • t
    • u
    • v
    • w
    • x
    • y
    • z
    002

    Laboratories

    Laboratory of Sound and Music Computing

    Optical and Electronic Technologies Research Laboratory

    Publications

    CTM Publications

    View all Publications

    2025

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

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

    Publication
    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.

    CloseRead Abstract

    2025

    CNN explanation methods for ordinal regression tasks

    Authors
    Gómez, JB; Cruz, RPM; Cardoso, JS; Gutiérrez, PA; Martínez, CH;

    Publication
    Neurocomputing

    Abstract

    2025

    CNN explanation methods for ordinal regression tasks

    Authors
    Barbero-Gómez, J; Cruz, RPM; Cardoso, JS; Gutiérrez, PA; Hervás-Martínez, C;

    Publication
    NEUROCOMPUTING

    Abstract
    The use of Convolutional Neural Network (CNN) models for image classification tasks has gained significant popularity. However, the lack of interpretability in CNN models poses challenges for debugging and validation. To address this issue, various explanation methods have been developed to provide insights into CNN models. This paper focuses on the validity of these explanation methods for ordinal regression tasks, where the classes have a predefined order relationship. Different modifications are proposed for two explanation methods to exploit the ordinal relationships between classes: Grad-CAM based on Ordinal Binary Decomposition (GradOBDCAM) and Ordinal Information Bottleneck Analysis (OIBA). The performance of these modified methods is compared to existing popular alternatives. Experimental results demonstrate that GradOBD-CAM outperforms other methods in terms of interpretability for three out of four datasets, while OIBA achieves superior performance compared to IBA.

    CloseRead Abstract

    2025

    Learning Ordinality in Semantic Segmentation

    Authors
    Cruz, RPM; Cristino, R; Cardoso, JS;

    Publication
    IEEE Access

    Abstract

    2025

    Interpretable AI for medical image analysis: methods, evaluation, and clinical considerations

    Authors
    Gonçalves, T; Hedström, A; Pahud de Mortanges, A; Li, X; Müller, H; Cardoso, JS; Reyes, M;

    Publication
    Trustworthy AI in Medical Imaging

    Abstract

    Facts & Figures

    82Researchers

    2016

    2R&D Employees

    2020

    11Proceedings in indexed conferences

    2020

    View all Facts & Figures
    Contacts
    Filipe André Ribeiro

    Coordinator

    +351222094299

    filipe.a.ribeiro@inesctec.pt