KIMA: Noise

A participatory art piece on the effect of noise on health and wellbein

KIMA Voice

KIMA: Voice is a participatory art piece enquiring the role of the arts in perceived social connectednes

Connections - Participatory Art as Facilitator for Social Cohesion

The potential for Participatory Arts to contribute to Mental Health and Wellbeing has been subject of Parliamentary Debates, All-Party Interparliamentary reports, research by the Arts Council of England as well as academic research. Often, these questions stand in the light of accountability of Art, of measurable societal benefit, not at least to justify funding decisions and institutional support. Criticism of this quantitative reading of participatory arts centres around limitations in measuring social connectedness and its benefits, as well as other side-effects of reductionism (Bishop 2012). This paper presents four recent media arts projects aiming to contribute to social cohesion through a multitude of strategies. This paper discusses the potential of media arts to contribute to social connectedness as well as challenges in measuring their success

EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A Convolutional Autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.The authors acknowledge the National Cancer Institute and the Foundation for the National Institutes of Health for the free publicly available LIDC-IDRI Database used in this work. They also acknowledge The Cancer Imaging Archive (TCIA) for the open-access NSCLC-Radiogenomics dataset publicly available. This work was supported in part by the European Regional Development Fund (ERDF) through the Operational Program for Competitiveness and Internationalization—COMPETE 2020 Program, and in part by the National Funds through the Portuguese Funding Agency, Fundação para a Ciência e a Tecnologia (FCT), under Project POCI-01-0145-FEDER-030263